Rule based systems

This paper presents a survey of Machine translation system for Indian Regional languages. Machine translation is one of the central areas of Natural language processing (NLP). Machine translation (henceforth referred as MT) is important for breaking the language barrier and facilitating inter-lingual communication. For a multilingual country like INDIA which is largest democratic country in whole world, there is a big requirement of automatic machine translation system. With the advent of Information
Technology many documents and web pages are coming up in a local language so there is a large need of good MT systems to address all these issues in order to establish a proper communication between states and union governments to exchange information amongst the people of different states. This paper focuses on different Machine translation projects done in India along with their features and domain.

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Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental r...

Context awareness plays an important role in recent smart environments and embedded interactions. In order to increase user satisfaction and acceptance, these context-aware solutions should be controllable by end users. Over the last few years we have therefore seen an emerging trend towards visual programming tools for context-aware applications based on simple "IF this THEN that" rules. However, existing solutions often do not support the simple reuse of the "this" part in order to define more sophisticated rules. Given that the desired level of control varies among individuals, we propose a unified multi-layered context modelling approach distinguishing between end users, expert users and programmers. Our Context Modelling Toolkit (CMT) consists of the necessary context modelling concepts and offers a rule-based context processing engine. We further illustrate how end users and expert users might interact with the CMT framework. Finally, we highlight some advantages of our Context Modelling Toolkit by discussing a number of use cases.

Software industry has been rapidly growing from the last couple of decades. Due to this growth and advancement many issues are going to be occurred. There are number of factors that affect the whole software development process. This research has focused on the most important risk factors that affect the overall software project and risk mitigation reviewed from the literature and my own experience. The main aim of this research is to collect the most important risk factors and risk mitigation for the development of intelligent network using an Artificial Intelligence technique known as Rule Based System. This is helpful for the project managers to make decisions. For the accomplishment of this task Firstly an exploratory survey has been conducted to prioritize the twenty requirements and fifty software risk mitigation factors. A sample of 200 respondents is selected out of which 100 are the university students and 100 are the IT professionals of a well reputed software house. Secondly a Rule Based technique is used to generate the risk mitigation intelligent network. Rules have been stored in the form of risk mitigation in relationship with risk factors. The system follows five steps of execution. First of all risk factors has been prioritized based upon the associations of risk factors and risk mitigation. Secondly, it creates new relationships between them. Thirdly, enter these relationships as rules into the Knowledge Base or Rule Base. At the fourth stage, an RBS engine executes these rules and finally an intelligent risk mitigation network is generated.

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic
patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE
extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and
on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

Computer role-playing games (CRPGs) are known for their strong narrative structure. Over time, quests have become one of the main mechanics for leading a player through the story. Quests are given to the player in the form of a set of tasks to complete with few, if any, options. The options given to the player instead often revolve around combat-oriented actionsrequiring the player to engage in combat to progress through the storyline, despite player preference or game story that hints otherwise. We address this issue with the GrailGM, a run-time game master which offers quests and actions to the player based on their history and current world state.

The main aim of this research paper is to develop a rule based knowledge database for legal expert system
for consumer protection act, a domain within the Indian legal system which is often in demand. A
knowledge database developed here will further help the legal expert system to determine type of case with
respect to the Indian Judicial System. In this paper a rule based knowledge database will be developed to
determine the type of the case. The main aim of the study is to build a prototype which will rule based in
nature. The rule based knowledge database development is the first phase in the development of
comprehensive rule based legal expert system for consumer protection act which will be of great help in
process of solving consumer related cases.

This study was carried out to study the existing paper-based system of receiving information and managing international student’s information at the Office of International Programme (OIP), University of Ibadan and to design an only student information management system that will help the OIP in managing international students’ information efficiently and effectively. Agile Methodology was adopted. System analysis was carried out in order to collect factual data, understand the process involved in the old system, identify problems and recommend feasible suggestions for improving the functioning of the system. In studying the existing system (paper-based), direct and indirect techniques were used in obtaining data for the analysis of the system. The problems of the existing system were identified and Online Student Information Management System was considered a possible solution to the problem of the existing system. The new system provides quick and efficient storage and retrieval o...

In this article, we try to determine what makes a currency reform more or less likely to succeed. We focus on important currency transformations throughout history -starting from roman antiquity to 19th century currency reforms in Morocco-. We find that beyond the obvious macroeconomic reasons, the controllability of monetary production, the cultural acceptance of the new currency's traits and rules, and increasingly a degree of resistance to transnational power dynamics are key to the success of such endeavors.

Rule learning is one of the most popular areas in machine learning research, because the outcome of learning is to produce a set of rules, which not only provides accurate predictions but also shows a transparent process of mapping inputs to outputs. In general, rule learning approaches can be divided into two main types, namely, 'divide and conquer' and 'separate and conquer'. The former type of rule learning is also known as Top-Down Induction of Decision Trees, which means to learn a set of rules represented in the form of a decision tree. This approach results in the production of a large number of complex rules

The rule-based approach has successfully been used in developing many natural language processing systems. Systems that use rule-based transformations are based on a core of solid linguistic knowledge. The linguistic knowledge acquired for one natural language processing system may be reused to build knowledge required for a similar task in another system. The advantage of the rule-based approach over the corpus-based approach is clear for: 1) less-resourced languages, for which large corpora, possibly parallel or bilingual, with representative structures and entities are neither available nor easily affordable, and 2) for morphologically rich languages, which even with the availability of corpora suffer from data sparseness. These have motivated many researchers to fully or partially follow the rule- based approach in developing their Arabic natural processing tools and systems. In this paper we address our successful efforts that involved rule-based approach for different Arabic n...

Healthcare sector contains large volumes of data. A large volume of health related data is in the form of graphics, images and text. Clinical decision support system is an old area of research but current Electronic Medical Records (EMR) has no decision support system that could create a proper relationship between relevant treatments against their respective diagnoses. This research has a unique idea to incorporate decision support system in EMR. Paper based environment has already been replaced with EMR but still medical providers are facing problems by choosing less relevant procedures, which may not produce the optimal result. Due to this reason claims rejection rate are high and payments are low in numbers. Focus of this research work is to provide a decision support system and to deliver focused knowledge in the form of relationship between diagnoses and their procedures. The system follows three steps to make a decision. Firstly, it checks the past claims for diagnosis and tr...

Situation Awareness involves the comprehension of the state of a collection of objects in an evolving environment. This not only includes an understanding of the objects' characteristics but also an awareness of the significant relations that hold among the objects at any point in time. Systems for establishing situation awareness require a knowledge representation for these objects and relations. Traditional ontologies, as defined with a language like DAML/OWL, are commonly used for such purposes. Unfortunately, these languages are insufficient for describing the conditions under which specific relations might hold true, which requires the explicit representation of implications, as is provided by RuleML. This paper describes an approach to knowledge representation for situation awareness employing RuleML-based domain theories constructed over OWL ontologies, presented in the context of its implementation in a Situation Awareness Assistant under development by the authors. Suggestions are also made for additions to the RuleML specification.

Software industry has been rapidly growing from the last couple of decades. Due to this growth and advancement many issues are going to be occurred. There are number of factors that affect the whole software development process. This research has focused on the most important risk factors that affect the overall software project and risk mitigation reviewed from the literature and my own experience. The main aim of this research is to collect the most important risk factors and risk mitigation for the development of intelligent network using an Artificial Intelligence technique known as Rule Based System. This is helpful for the project managers to make decisions. For the accomplishment of this task Firstly an exploratory survey has been conducted to prioritize the twenty requirements and fifty software risk mitigation factors. A sample of 200 respondents is selected out of which 100 are the university students and 100 are the IT professionals of a well reputed software house. Secondly a Rule Based technique is used to generate the risk mitigation intelligent network. Rules have been stored in the form of risk mitigation in relationship with risk factors. The system follows five steps of execution. First of all risk factors has been prioritized based upon the associations of risk factors and risk mitigation. Secondly, it creates new relationships between them. Thirdly, enter these relationships as rules into the Knowledge Base or Rule Base. At the fourth stage, an RBS engine executes these rules and finally an intelligent risk mitigation network is generated.

To know finished priority scale of case in Laboratory of Forensic, in this thesis we developed an expert system with forward and backward chaining methods rule based, used some criteria of case. Priority scale of case is done to sort data and determine the stage of case, which will be used, for examination. This process needs an evaluation in a number on aspect in accordance with the criteria and characteristics of the case. The capabilities of this system accommodates input data cases which user needs, priority scale and stages according to the standard operating procedure in Indonesian National Police especially in Laboratory of Forensic branch in Semarang and gives the visual picture like groove determining of the priority scale also the best solution from the several alternatives used forward and backward chaining methods rule based. The result showed a good and ideal decision for solved case by quickly recommendation and main priority of case with the result of compatibility level was 100%.

The so-called hybrid expert systems for analysis of physical systems (e.g., mechanical) integrate modelling and simulation methods, including classical numerical (quantitative) methods as well as recently developed qualitative analysis methods, using the expert-system rule-based techniques . The diagrammatic representation methods also turn out to be important elements of knowledge representation (especially the qualitative knowledge) and user interface in such systems. In the paper, problems of construction of such systems are discussed, using an example of the experimental hybrid system for analysis of planar truss structures (currently under implementation).

Rule learning is one of the most popular areas in machine learning research, because the outcome of learning is to produce a set of rules, which not only provides accurate predictions but also shows a transparent process of mapping inputs to outputs. In general, rule learning approaches can be divided into two main types, namely, 'divide and conquer' and 'separate and conquer'. The former type of rule learning is also known as Top-Down Induction of Decision Trees, which means to learn a set of rules represented in the form of a decision tree. This approach results in the production of a large number of complex rules (usually due to the replicated sub-tree problem), which lowers the computational efficiency in both the training and testing stages, and leads to the overfitting of training data. Due to this problem, researchers have been gradually motivated to develop 'separate and conquer' rule learning approaches, also known as covering approaches, by learning a set of rules on a sequential basis. In particular, a rule is learned and the instances covered by this rule are deleted from the training set, such that the learning of the next rule is based on a smaller training set. In this paper, we propose a new algorithm, GIBRG, which employs Gini-Index to measure the quality of each rule being learned, in the context of 'separate and conquer' rule learning. Our experiments show that the proposed algorithm outperforms both decision tree learning algorithms (C4.5, CART) and 'separate and conquer' approaches (Prism). In addition, it also leads to a smaller number of rules and rule terms, thus being more computationally efficient and less prone to overfitting.

brief and precise, outlining only the most relevant topics and the proposed objectives):  Carlos III University (Pedro Isasi, evolutionary neural networks and instance selection)  European Center of Soft Computing, Mieres (Oscar Cordón and Luis Magdalena, genetic fuzzy systems)  Polytechnic University of Madrid (Pedro Larrañaga, evolutionary learning of Bayesian netwoks, José M. Peña, parallel data mining)  Ramón Llull University (Josep Mª Garrell and Ester Bernardó, learning classifier systems and data complexity)  University of Asturias (Luciano Sánchez, genetic fuzzy systems)  University of Castilla la Mancha (José Antonio Gámez, causal networks and fuzzy systems)  University of Córdoba (César Hervás, evolutionary neural networks)  University of Extremadura (Francisco Fernández, genetic programming based learning)  University of Granada (Francisco Herrera -knowledge discovery with soft computing techniques, Antonio González -genetic fuzzy systems, and Juan Julián Merelo -evolutionary neural networks, evolutionary data streams)  University of Jaén (María José del Jesus, association rule mining, genetic fuzzy systems)  University of Málaga (Enrique Alba and Carlos Cotta, evolutionary neural networks and data analysis of bioinformatics)  University of País Vasco (Jose Antonio Lozano, evolutionary learning of Bayesian networks and EDA)  University of Santiago de Compostela (Alberto Bugarín and Manuel Mucientes, genetic fuzzy systems and robotics)  University of Sevilla (José Riquelme, evolutionary learning of rules)

CIRCUS is a conceptual analyzer that produces semantic case frame representations for input sentences . Although space does not permit us to give a full technical description of CIRCUS, we will attempt to convey som e sense of sentence analysis via CIRCUS . For more details, please consult [2] and [1].

Abstract: Gujarati is a resource poor language with almost no language processing tools being available. In this paper we have shown an implementation of a rule based stemmer of Gujarati. We have shown the creation of rules for stemming and the richness in morphology that Gujarati possesses. We have also evaluated our results by verifying it with a human expert.

Abstract. Institutional change explains the change of institutions considered as rules and expectations that govern human interactions and paths of development in society. This conceptual paper describes, with an historical perspective, the most important definitions of institution and of institutional change and shows some theories that analyze these critical topics in economics to assess similarities and differences. The future challenge of institutional scholars concludes this study. Keywords. Institutions, Institutional theory, Deinstitutionalization. JEL. D02, E02, G20, I20.

Process mining is a technique for extracting process models from event logs. Process mining can be used to discover, monitor and to improve real business processes by extracting knowledge from event logs available in process-aware information systems. This paper is concerned with the problem of grouping events in instances and the preparation of data for the process mining analysis. Often information systems do not store a unique identifier of the case instance, or errors happen in the system during the recording of events in the log files. To be able to analyze the process, it is necessary that events are grouped into case instances. The aim of the presented rule based algorithm is to find events belonging to the same case instance. Performances of the algorithm, for different sizes of log file events and different levels of errors within log files in the real process, have been analyzed.

This paper presents a real-world application targeting energy savings and user comfort in a Smart Building environment. The proposed system uses various heterogeneous networks of wireless sensors and actuators, while all functions and data are unified under a Semantic Web Service middleware. An initial approach to author and enforce energy-saving policies was to develop a reactive agent that maintains an internal representation of the world"s state in the form of predicates and facts, gathered over the Web Service interface. System administrators are able to encode the policies, the effects of which are applied by invoking actuator Web Services. In order to improve the system even further, the proposed architecture also incorporates a deliberative agent based on defeasible logic that allows for more human-intuitive and effective management of policies. The application of defeasible reasoning is ideal in such environments with incomplete and contradictory information, offering more flexible expressiveness as well as a sophisticated conflict resolution mechanism. In our approach, the latter is used for defining three clusters of rules for preferences, maintenance and emergencies, respectively. After observing the building"s yearly energy consumption and usage patterns, a policy was authored and applied, showing promising results (at least 10kWh or 2% to 4% daily savings in this specific setting). Although the percentage of energy savings may seem low, the greatest merit of this method is ensuring no energy is wasted by constantly enforcing the policies.

When someone in his audience said, Convince me that logic is necessary, he answered: Do you wish me to demonstrate this to you?-Yes.-Well, then, must I use a demonstrative argument?-And when the questioner had agreed to that, Epictetus asked him, How, then, will you know if I impose upon you?-As the man had no answer to give, Epictetus said: Do you see how you yourself admit that all this instruction is necessary, if, without it, you cannot so much as know whether it is necessary or not?

In this paper we present a comparison between two novel approaches to the fundamental problem of cyclic instability in ambient intelligence. These approaches are based on two optimization algorithms, Particle Swarm Optimization (PSO) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of average accumulative oscillation, which enabled us to measure the average oscillatory behaviour of the system. PSO and MIMIC have the advantage that they do not need to analyze the topological properties of the system, in particular the loops. In order to test these algorithms we used the well-known discrete system called the game of life for 9, 25, 49 and 289 agents. It was found that PSO performed better than MIMIC in terms of the number of agents blocked. These results were confirmed using the Wilcoxon test. This novel and successful approach is very promising, and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies between them.

TacAir-Soar is an intelligent, rule-based system that generates believable "human-like" behavior for large scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the United States military flies in fixed-wing aircraft. It accomplishes this by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currently deployed at the Oceana Naval Air Station WISSARD Lab and the Air Force Research Laboratory in Mesa, AZ. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TacAir-Soar flew all U.S. fixed-wing aircraft.

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global perspective. First, we formulate a broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables. This novel model class allows us to formalize the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalized Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Notably, we show that our problem definition is equal to mining the top-1 subgroup with an information-theoret...

In image analysis, there have been few effective procedures that deal with a wide class of imagery acquired by different sensors under ditrerent environmental conditions. The success of a given classification algorithm is dependent upon pattern familiarity, background, and the image acquisition process.

In this work, we present a hybrid classification technique combining an expert system and an object‐oriented
approach. The expert system allows the integration of a knowledge base built through a series of deductive rules, that will
guide the classification whose primitives requires informations on the highest level and will be represented by semantic
objects, not pixels. Instead of the original bands only, other derived data combining textural, spectral information and
shapes, are included in the classification process. The result is then combined with an expert system whose rules use
variables such as vegetation index (NDVI), shading of building objects and other indicators. In conclusion, this approach has
allowed us to improve the accuracy of the feature extraction method by extracting objects like, roads, trees, grass, bare soil
and shadow on a very high‐resolution image of the city of Rabat.

Frequent-Pattern Tree (FP-Tree) algorithm plays a vital role in mining associations, patterns and other data mining related jobs. Currently a software risk mitigation intelligent decision network engine using rule based technique has been designed against software risk factors. A novel idea of finding the associations between different software risk mitigation factors using FP-Tree algorithm has been proposed in this research work. Huge volumes of software risk factors and software risk mitigation factors exist in software development industry. That's why data mining researchers have been attracted towards it to find something to explore. Software risk mitigation frequent patterns will be helpful for the project managers and software developers to make decisions against the software risk factors.

We believe that the score reports we obtained for TST2 provide an accurate assessment of ou r system's capabilities insofar as they are consistent with the results of our own internal tests conducted near the end of phase 2 . . The required TST2 score reports indicate that our system achieved the highest combined scores for recall (51%) and precision (62%) as well as the highest recall score of all the MUC-3 systems under the official MATCHED/MISSING scoring profile .

In this paper we describe how the Predictive Model Markup Language (PMML) standard enhances the JBoss Drools production rule engine with native support for using predictive models in business rules. The historic debate between symbolic and connectionist approaches to rule/model orchestration provides numerous examples of hybrid systems combining "hard" and "soft" computing techniques to achieve different levels of integration. Rules

Truly autonomous vehicles will require both projective planning and reactive components in order to perform robustly. Projective components are needed for long-term planning and replanning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real-time, and themselves can exhibit robust behavior, but lack the ability to explicitly reason about future states over a long time period. This work addresses the problem of creating reactive components for autonomous vehicles. Creating reactive behaviors (stimulus-response rules) is generally difficult, requiring the acquisition of much knowledge from domain experts, a problem referred to as the knowledge acquisition bottleneck. SAMUEL is a system that learns reactive behaviors for autonomous agents. SAMUEL learns these behaviors under simulation, automating the process of creating stimulus-response rules and therefore reducing the bottleneck. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. Current work is investigating how well behaviors learned under simulation environments work in real world environments. In this paper, we describe SAMUEL, and describe behaviors that have been learned for simulated autonomous aircraft, autonomous underwater vehicles, and robots. These behaviors include dog fighting, missile evasion, tracking, navigation, and obstacle avoidance.

Multi-agent systems underpin the vision for ambient intelligence. However, developing multi-agent systems is a complex and challenging process. For example, pervasive computing has been found susceptible to instability, due to unwanted behaviour arising from unplanned interaction between rule based agents. This instability is impossible to predict, as it depends on the rules of interaction, the initial state of the system, the user interaction, and in the time delay of the system (due to network traffic, different speed of processing, etc). In this paper we present a theoretical framework, an Interaction Network (IN), together with a communication locking strategy that we call INPRES (Instability Prevention System) that can be used to identify and eliminate this problem. In addition we describe a Multi-Dimensional Model (MDM) to represent the agents and the state of each agent over time. A theorem showing the role of delays in an unstable system is presented. We present experimental results based on simulations and a physical emulation that demonstrate the effectiveness of these methods. system) and the perturbations to the system (when the user interacts with the environment) add significant complexity to the dynamics or the system. However, it is possible to detect and prevent cyclic instability and we have developed and tested such a strategy, based on algorithms for finding loops in an Interaction Network and locking devices with the least functional impact on the performance of the system.

A decision support tool is presented that is especially tailored for architecting Earth observing missions and programs. The tool features both a cost model and a performance model. This paper focuses on the description of the performance model. Indeed, while considerable effort has been put into the development of cost estimating models, comparably much less effort has been put into the development of quantitative methods to assess how well Earth Observing Mission satisfy scientific and societal needs. A literature review revealed that existing methods include a commercial approach, a value-of-information approach, end-to-end simulation, assimilation in Observing System Simulation Experiments, and simple expert judgment. Limitations of these methods include limited applicability, computational complexity, low modeling fidelity (e.g. abstraction of synergies between measurements), and subjectivity. Our method uses a knowledge-based system to store and manage large quantities of expert knowledge in the form of rules-of-thumb that replace expensive computations.

Prism has been developed as a modular classification rule generator following the separate and conquer approach since 1987 due to the replicated sub-tree problem occurring in Top-Down Induction of Decision Trees (TDIDT). A series of experiments have been done to compare the performance between Prism and TDIDT which proved that Prism may generally provide a similar level of accuracy as TDIDT but with fewer rules and fewer terms per rule. In addition, Prism is generally more tolerant to noise with consistently better accuracy than TDIDT. However, the authors have identified through some experiments that Prism may also give rule sets which tend to underfit training sets in some cases. This paper introduces a new modular classification rule generator, which follows the separate and conquer approach, in order to avoid the problems which arise with Prism. In this paper, the authors review the Prism method and its advantages compared to TDIDT as well as its disadvantages that are overcome by a new method using Information Entropy Based Rule Generation (IEBRG). The authors also set up an experimental study on the performance of the new method in classification accuracy and computational efficiency. The method is also evaluated comparatively with Prism.

The subject of this paper is the control of autonomous vehicles. A hierarchical approach is studied in the context of fuzzy systems and a programming language for the mid to low level control of autonomous vehicles is described. The language, called FDTL (Fuzzy Decision Tree Language), is based on a computational model that combines fuzzy rule based control with the hierarchical nature of decision trees.

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfitting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances . This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suitable structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations: rule generation, rule simplification and rule representation particularly on Big Data. The authors also review some existing methods and techniques used for each of the three operations and highlight the limitations of them as well as introduce some novel methods and techniques developed in their more recent research. The novel methods and techniques are also discussed in comparison to those existing ones reviewed earlier with respects to effective and efficient processing of Big Data.

Considerable interest has existed in the literature for a long time in support of real-time crack detection Non-Destructive Techniques (NDT) in a variety of applications. Various authors [1, 15, 16, and 17] have suggested different approaches in finding a solution to this problem. In this paper we suggest a Soft Computing approach using Fuzzy Logic as an effective method to take up this problem. It is also desirable to develop chip which can be used for detection of crack. The objective of this paper is to report on efforts to develop an automated procedure to detect the crack and to devise technique so that the proposed method can be easily implemented on a chip. The Fuzzy Inference System developed is supported by the rule base. A Very Large Scale Integration (VLSI) circuit for the detection of crack is developed on the basis of the Fuzzy Logic Model using a Hardware Description Language (HDL). Field-Programmable Gate Array (FPGA) implementation and testing of this circuit is done.

Healthcare sector contains large volumes of data. A large volume of health related data is in the form of graphics, images and text. Clinical decision support system is an old area of research but current Electronic Medical Records (EMR) has no decision support system that could create a proper relationship between relevant treatments against their respective diagnoses. This research has a unique idea to incorporate decision support system in EMR. Paper based environment has already been replaced with EMR but still medical providers are facing problems by choosing less relevant procedures, which may not produce the optimal result. Due to this reason claims rejection rate are high and payments are low in numbers. Focus of this research work is to provide a decision support system and to deliver focused knowledge in the form of relationship between diagnoses and their procedures. The system follows three steps to make a decision. Firstly, it checks the past claims for diagnosis and tr...

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a framework to evolve rules automatically. The objective of the framework is to reduce the development effort and guide the development procedure in user friendly fashion. The architecture presents a novel approach which integrates fuzzy logic with system design as well as back end of the framework is used to evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed framework, a system to measure multiple intelligence is selected. The paper describes the detail methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve fuzzy rules for the selected system. The initial population of rules, evolved generations and output results are also described. The paper concludes with the scope and applications of ...