Rule based systems

In some applications, parallel operation of some single-phase inverters with different characteristics is a necessity, such as in a photovoltaic power conversion system. Each power inverter with its power source works, delivering power to a common load which cannot be supplied by a single power inverter. This paper proposed a novel parallel operation of two different power inverter circuit types. H-bridge voltage source inverter (HB-VSI) and H-bridge current source inverter (HB-CSI), supplying AC power to a common load. The proposed inverter system was examined and its operation characteristics were analyzed using computer simulation. Moreover, a laboratory prototype of the inverter system was made and examined to validate some principal characteristics of the inverter system experimentally. Test results showed that by combining the HB-VSI and HB-CSI, a lower distortion of load current was achieved, specifically, total harmonic distortion (THD) of Iload was less than 1%. This phenomenon happens even the THD of AC currents generated by HB-VSI and HB-CSI at 6.95% and 6.18%, respectively.

Induction motors are widely used in industrial power plants because of their durability, reliability and high performance under different operating conditions of the electrical system. It is also important to note that most of these motors are controlled by variable frequency drives. By adjusting the drive parameters, the motor can be managed according to design. The reliability of motor control systems based on variable speed drives is therefore crucial for industrial applications. Unlike induction motors, the power supply components of these electrical machines are delicate and susceptible to faults. To enhance the performance of the control-motor system, it is essential for researchers to understand how faults affect the drive system as a whole. In this context, this paper addresses short-circuit faults in the intermediate circuit capacitor of an induction motor driven by an inverter. The simulation results of these capacitors faults are presented, and their impact on the behavior of the rectifier, the inverter, and the induction motor is analyzed and interpreted.

In this work we investigate the signal contained in the language of food on social media. We experiment with a dataset of 24 million food-related tweets, and make several observations. First, the language of food has predictive power. We are able to predict if states in the United States (US) are above the median rates for type 2 diabetes mellitus (T2DM), income, poverty, and education -outperforming previous work by 4-18%. Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates. Socioeconomic factors do improve T2DM prediction, with the greatest improvement coming from poverty information (6%), but, importantly, the language of food adds distinct information that is not captured by socioeconomics. Third, we analyze how the language of food has changed over a five-year period (2013 -2017), which is indicative of the shift in eating habits in the US during that period. We find several food trends, and that the language of food is used differently by different groups such as different genders. Last, we provide an online visualization tool for real-time queries and semantic analysis.

classification technique to help the marketing department of Higher Educational Institutions identify probable secondary schools for recruitment. A mapping feature was also provided to show possible routes from the point of origin to the target institution/s. This is to maximize the visit for every probable school. In view of this tool, the cost incurred by these educational institutions can be eliminated, if not, alleviated. Likewise, the application helps the administration recognize some qualified secondary schools for School-to-School Promotion (STSP) and utilize other resources.

approach with an additional view that represents the semantic perspective to the architecture, the overall system design is presented comprehensively. 'Measurement' 'has_meas_system' 'Meas_system'. The 'has' relation is defined by the user, hence it is represented by the dash lines. The generalisation relations between the 'Quantity_value'

Due to the rapid growth in the field of Internet, the related security mechanisms are the key area of research. Firewalls serve the solution for secured Internet experience. Latest firewalls are fully-equipped for providing hi-end security to the network. However, due to the continuous growth of security threats, the firewall mechanisms and policies are compulsorily needed to get updated. The manual processing for detecting anomalies in firewall is complex and often error-prone. Any minor change in the rule set of firewall leads to the requirement of rigorous analysis for maintaining the consistency and efficiency of firewall mechanism. Many Data structures have been proposed for detection and removal of anomalies so as to reduce the burden of Network Administrator. In this paper I have shown the results of implementation of a mechanism for the anomalies detection in the centralized and distributed firewall systems. This paper also discusses the design implementation of the irrelevance anomaly for the intra firewalls. It is developed in VB.Net and SQL Server. The algorithm used in this paper purifies the rule sets of firewall in such a way that makes the rule set optimal and free from all known anomalies.

The reasoning on this hybrid ontology makes it possible to improve in terms of results the quality of the previous reasoning. To mention and clarify the importance of this kind of reasoning, we use a case study inherent to interpret a check up in preventive medicine.

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 are often used to decide when and which model to invoke; model outputs, in turn, can be used to evaluate the preconditions of a rule. In a loosely coupled system, the rule engine calls an external component implementing the predictive model, but this has several disadvantages, most notably the need to setup proper communications and reconcile any difference in the way the components encode the data. We propose instead, a tightly integrated system where predictive models and rules become part of the same reasoning framework. The models, encoded using the PMML 4 standard, are loaded and processed by a compiler implemented using the rule engine itself. The PMML document is transformed into a set of facts that define the model, and a series of rules that formalize the model's behavior. In addition, most PMML data processing, validation, and transformation procedures are also implemented using auto-generated rules. Finally, in oder to integrate model inputs and outputs seamlessly in the inference process, we exploit an extension of the Drools engine which adds native support for uncertainty and/or fuzziness.

The paper presents automatic translation of noun phrases from Punjabi to English using transfer approach. The system has analysis, translation and synthesis component. The steps involved are pre processing, tagging, ambiguity resolution, translation and synthesis of words in target language. The accuracy is calculated for each step and the overall accuracy of the system is calculated to be about 85% for a particular type of noun phrases.

 Learning refers to the process where algorithms, models, or systems adapt and improve their performance based on data input and experience.  "Tree" refers to a decision tree; a supervised learning algorithm used for both classification and regression tasks.  It's a flowchart-like structure where each node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a predicted value.  Tree-based algorithms are a fundamental component of machine learning, offering intuitive decision-making processes to human reasoning.  These algorithms construct decision trees, where each branch represents a decision based on features, ultimately leading to a prediction or classification.  By recursively partitioning the feature space, tree-based algorithms provide transparent and interpretable models, making them widely utilized in various applications.  Tree-based algorithms are a class of supervised machine learning models that construct decision trees to typically partition the feature space into regions, enabling a hierarchical representation of complex relationships between input variables and output labels.  Some of tree based algorithm are i. Random forests ii. Gradient Boosting techniques iii. Decision trees iv. Gini impurity v. Information gain The main four workflows of tree-based algorithms are discussed below: 1. Feature Splitting: Tree-based algorithms begin by selecting more informative features to split a data set based on a specific criterion, such as Gini impurity or information gain etc. 2. Recursive splitting: The selected feature of dataset is used to split the data in two, and the process is repeated for each resulting subset, forming a hierarchical binary tree structure. This recursive splitting until stops a predefined criterion, like a maximum depth or a minimum number of samples per train data, is met as long as it lasts. 3. Leaf Node Function: As the tree grows, each terminal node (leaf) is given a predicted outcome based on majority learning (for classification) or the sample value of that node of the (for regression). This activates the tree to capture complex decision boundaries and relationships in the data. For ensemble methods like Random Forests and Gradient Boosting Machines, multiple trees are trained independently, and their predictions are combined to obtain the final result. This group approach helps to reduce over fitting, increase generalization, and improve overall model performance by combining the strengths of individual trees and reducing their weaknesses.  Decision Tree is a supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.  It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.  A decision tree can contain categorical data (YES/NO) as well as numeric data.  Decision tree is graphical representation for getting all the possible solutions to a problem/decision based on given conditions.  There are two nodes, which are the Decision Node and Leaf Node.  Decision nodes are used to make any decision and have multiple branches.  Leaf nodes are the output of those decisions and do not contain any further branches.

In the following, we focus on the exploration of DNA bio-molecules as prospective candidates for molecular electronic devices [1]. For the scientists devoted to the investigation of charge mobility in DNA, a no less important motivation than the strong technological drive is that DNA molecules comprise an excellent model system for charge transport in one-dimensional polymers. This most well-known polymer enables an endless number of structural manipulations in which charge transport mechanisms like hopping and tunneling may be studied in a controlled way. The DNA materials having the Semiconducting and conducting properties for their proper configuration. In this paper we describe the importance of DNA molecules as Semiconductor and Conductor by their appropriate configuration.

The distribution of money was transformed into the concept of debt in various forms, including the use of money as an instrument of loans and gifts. The rules of money have completely changed and are heavily skewed towards the benefit of the rich and powerful. Has begun. The US dollar is no longer money, but a currency. The Britton Woods agreement, the gold-standard dollar is over... We Gave Our Word To You But We Don't Have To Keep It 1 The dollar became Monopoly Money. After that, the biggest economic boom in history began. In 2009, when the economy ran aground, the central bankers of the world created trillions of dollars, yen, pesos, euros, and pounds by following the bankers' monopoly. This concept has changed to this day. Investors are panning nothingness into existence. To this day, Investors do not carry piles of money in suitcases, only the game of money in a space called financial institutions (banks and non-banks). The escalation of financial investment dragged the country into debt because the country's economic strength was supported by printing debt securities faster to make money, so the government needed more money than savings. 2 BRICS is heading towards the elimination of fiat money, preventing member countries from falling into debt. Returning the existence of currency to its history, namely money as a medium of exchange, not a means of payment (fiat money). The essence of the idea of the birth of new money for BRICS members is a clever concept of distorting debt-based money. It is better to have a lot of receivables than to have a lot of debts. This study aims to understand the transition from the gold standard system to a sovereign debt-based system, specifically within the context of currency and economic influence of BRICS countries (Brazil, Russia, India, China, and South Africa). Against the backdrop of a global economy shifting from precious metals to fiat and debt-based currencies, this research will examine the complexities and paradoxes behind this change and its impact on current economic stability and sustainability

Information retrieval technology has been central to the success of the Web. For semantic web documents or annotations to have an impact, they will have to be compatible with Web based indexing and retrieval technology. We discuss some of the underlying problems and issues central to extending information retrieval systems to handle annotations in semantic web languages There is need to organize that data in formal manner so that user can easily exploit them. To retrieve information from documents, we have many Information Retrieval techniques. Current Information Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge within documents and give precise result. IR technology is major factor responsible for handling annotations in Semantic Web (SW) languages. Knowledgeable representation languages are used for retrieving information.

Nowadays, gardening is one of the sophisticated hobbies and also very important for environment balance. But the main problem is the time adjustment for watering the plants. Especially when people are aways from home for few days, then lots of problem arises to watering the plants. In this technological era where people rely on the technology for almost everything to make their work easier and more efficient, then why not there has been a system that automatically waters the garden and plants without depending on someone when one is there at home for longer period. So, one of the activities are replaced from automated irrigation and also excess use of water is minimized. The method is employed, to continuously keep on eye on the soil moisture level to decide whether irrigation is needed, and how much water is needed for the soil. Programmed code for this paper is based on Arduino UNO C++ language. If dryness level is high, moisture level is low. At that time, the water pump is switched ON to supply water to the plant and is automatically OFF when the soil moisture reaches to the minimum threshold level. The total work is done through this subsystem; Soil moisture sensor, Water pump, and Arduino UNO

In this paper we investigate the relation between legal knowledge-based systems and large-scale computer networks such as the Internet. On the one hand, researchers of legal knowledge-based systems have claimed huge possibilities, but despite the efforts over the last twenty years, the number of practical applications actually in use is still small. On the other hand the computer networks, which in a practical form are only around for a few years, are gaining momentum and are expected to have a serious impact on many fields of society, including the legal domain. We look at the developments in both fields and ask the question: What can researchers of legal knowledge-based systems learn from the recent developments on the Net in order to improve the practical application of their research? 1 Computer support for the legal domain Modern computers have lost the intuitive connection with the notion of ‘computing’. With the help of computers and computer networks, we are nowadays able to...

One of the crucial aspects of the environmental protection is continuous monitoring of environment. Specific aspect is estimation of the bird species population. It is particularly important for bird species being in danger of extinction. Avian monitoring programs are time and money consuming actions which usually base on terrain expeditions. Certain remedy for this can be automatic acoustical avian monitoring system, described in the paper. Main components of the designed system are: digital audio recorder for bird voices acquisition, computer program automatically recognizing bird species by its signals emitted (voices or others) and object-relational database accessed via the Internet. Optional system components can be: digital camera and camcorder, bird attracting device, wireless data transmission module, power supply with solar panel, portable weather station. The system records bird voices and sends the recordings to the database. Recorded bird voices can be also provoked by ...

Most of the research in the area of performance evaluation of rule-based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed "benefit" as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are "claim rejection rate"-reduced by 54%-and "claim aging," which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.

This special issue of Knowledge Based Systems comprises expanded versions of the best papers submitted to the conference AI-2010, the thirtieth SGAI International Conference on Artificial Intelligence, which was held in Cambridge, England from December 14th to 16th 2010. The original papers were judged by the conference's programme committees to be the best refereed papers accepted for each of the two streams: six from the technical stream and five papers from the application stream. All of the papers included here have been re-refereed in their expanded forms.

One of the major 'weaknesses of current automated reasoning systems is that they lack the ability to con trol inference in a sophisticated, context-directed fashion. General strategies such as the set-of-support strategy are useful, but have proven inadequate for many individual problems. A strategy component is needed that possesses knowledge about many particu lar domains and problems. Such a body of knowledge would require a prohibitive amount of time to con struct by hand. This leads us to consider means of automatically acquiring control knowledge from exam ple proofs. One particular means of learning is explanation-based learning. This paper analyzes the basis of explanations -finding weakest preconditions that enable a particular rule to fire -to derive a representation within which explanations can be extracted from examples, generalized and used to guide the actions of a problem-solving system.

Many healthcare providers and clinicians are using various types of systems known as CDSS which assist them to take decision about each case of patient. There is increasing recognition that if CDSS is well planned and put into service, has an immense potential to get better health care quality and possibly even increase effectiveness and decrease healthcare costs. Some existing CDSS are reviewed in this paper. The various types of CDSS available encompass a range of preferences, from general references, through precise detailed guidelines for a given condition. Technological intervention in developing CDSS has to be evaluated while designing, developing and implementing it. The basic CDSS is designed and proposed outline is given. The implementation issues for CDSS have to be considered and for successful execution of CDSS meticulous planning has to be done. Healthcare providers and users of CDSS are required to understand CDSS benefits and limitations, and the inimitable challenges...

Natural language processing which is abbreviate as NLP. It is a prominent area of research now days. NLP's research and application discovers how computer can be used to understand and manipulate natural language's speech & text to do some interesting and useful things. The term language in Natural Language Processing (NLP) has to be understood as natural languages like (Gujarati, Hindi, English etc...). The center of attention of this paper is to get the review of available technology and its proper method to apply the some of the aspects of NLP for the implementation of Gujarati Grammar. Our main focus is on the Rule base implementation because similar to every Indian scripting language Gujarati also have its own specified rules of composition for combining the consonants, vowels and modifiers.

This report is published in the interest of scientific and technical information exchange and does not constitute approval or disapproval of its ideas or findings. Using Government drawings, specifications, or other data included in this document for any purpose other than Government-related procurement does not in any way obligate the US Government. The fact that the Government formulated or supplied the drawings, specifications, or other data, does not license the holder or any other person or corporation, or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them.

Issues such as project management, documentation, and maintenance, typically associated with large traditional software development projects have recently become important concerns in the development of expert systems. We describe a structured methodology for the development of production rule-based expert systems. The methodology prescribes a procedure to help knowledge engineers manage the complexity of a knowledge base by viewing rule and parameter dependencies at successive levels of abstraction. Parameters are clustered into modules, in a manner analogous to functional decomposition in structured design. The modularization thus obtained provides three significant benefits: ease of subsequent knowledge base maintenance, enhancement of code reusability, and support for knowledge base verification. The visual tools used by the methodology facilitate communication between knowledge engineers and experts and provide ongoing documentation of the system.

The area of Artificial Intelligence is very useful in providing people with a machine, which understands diverse languages spoken by the common man. It presents the user with an interface, with which he feels more comfortable. Since, there are many different languages spoken in this world, we are constantly in need for translators to enable people speaking different languages to share ideas and communicate with one another. English is the global language .The most of the information is available in English. The India is a country which has several regional languages. Sanskrit is the mother of all native language of India. A great storage of knowledge with subjects like medicine, mathematics, Geography, Geology, Astronomy, philosophy and many others is kept alive and fresh Sanskrit lore for thousands of years.English to sanskrit translator and Synthesizer is very useful to people in India , sentence in English is translated in to sanskrit using rule based approach and from sanskrit i...

End-user programming concepts are increasingly employed in smart home research to address the growing complexity of controlling smart home environments. Different approaches and visual styles of end-user programming have been proposed and implemented within this context. Smart home control does however not only necessitate end-user programming but also understanding and modifying existing program structures. In this study, we compare three different approaches regarding their suitability for this application context with a specific focus on intelligibility and modification performance. We conducted an empirical study with 39 users performing three types of tasks (understanding, configuring, and programming), using three different approaches for end-user programming (form-filling, data-flow, and grid-canvas). The results of our study found no significant differences regarding the intelligibility of the three different implementations but clear differences in the subjective preference...

Rule-based forecasting (RBF) is an expert system that uses features of time series to select and weight extrapolation techniques. Thus, it is dependent upon the identification of features of the time series. Judgmental coding of these features is expensive and the reliability of the ratings is modest. We developed and automated heuristics to detect six features that had previously been judgmentally identified in RBF: outliers, level shifts, change in basic trend, unstable recent trend, unusual last observation, and functional form. These heuristics rely on simple statistics such as first differences and regression estimates. In general, there was agreement between automated and judgmental codings for all features other than functional form. Heuristic coding was more sensitive than judgment and consequently, identified more series with a certain feature than judgmental coding. We compared forecast accuracy using automated codings with that using judgmental codings across 122 series. Forecasts were produced for six horizons, resulting in a total of 732 forecasts. Accuracy for 30% of the 122 annual time series was similar to that reported for RBF. For the remaining series, there were as many that did better with automated feature detection as there were that did worse. In other words, the use of automated feature detection heuristics reduced the costs of using RBF without negatively affecting forecast accuracy.

This paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule-based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule-based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise. Comments

This paper proposes Dynamic Mesh Optimization for the classical Facility Location Problem, we introduce this meta-heuristic which is a technique of evolutionary computation. A set of nodes that represent potential location solutions conform a mesh; it grows and moves dynamically throughout the search space. The algorithm performance has been compared with data set from literature. Computational results confirm the efficiency of the strategy we propose.

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. TFR represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit fault...

This paper focuses on stemming techniques used by various researchers for English language. Further the review of different approaches used for Hindi and Bengali languages are discussed. The authors have proposed a model for Gujarati stemmer by incorporating traditional as well as naïve approaches.

Firewall secures a private network from intrusions from other networks. The firewall has ACLs (Access Control List) that contain rules used to allow or deny incoming traffic. These rules form the security policy of the firewall. The large size and complexity of modern networks result in large and complex firewall policies. Designing policies for a network of firewalls is a difficult task as a number of cases have to be taken into consideration for access control. Also, a network administrator may want to update the policies in order to replace them with new ones. The process of updating firewall policies is difficult and error prone. In this paper, we provide a structured and comprehensive overview of various techniques in regards to firewall anomaly detection. We briefly describe and compare various known algorithms and tools used to detect and/or resolve the firewall anomalies.

secures a private network from intrusions from other networks. The firewall has ACLs (Access Control List) that contain rules used to allow or deny incoming traffic. These rules form the security policy of the firewall. The large size and complexity of modern networks result in large and complex firewall policies. Designing policies for a network of firewalls is a difficult task as a number of cases have to be taken into consideration for access control. Also, a network administrator may want to update the policies in order to replace them with new ones. The process of updating firewall policies is difficult and error prone. In this paper, we provide a structured and comprehensive overview of various techniques in regards to firewall anomaly detection. We briefly describe and compare various known algorithms and tools used to detect and/or resolve the firewall anomalies.

This paper presents a survey of Machine translation system for Indian Regional languages to English language. Machine translation is important for breaking language barrier. Out of 1.3 billion India population, hardly 10% people can speak, read and write English with varied degree of proficiency. 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 discusses the various approaches which have been applied in translation systems for Indian languages to English languages. Some of the important Indian language translation systems implemented with these techniques along with their capabilities and limitations are also discussed.

Rule-based systems or production systems still have great importance in the construction of knowledge systems. In these systems, the domain expertise to solve a problem is encoded in the form of 'if -then' rules, enabling a modular description of the knowledge, thus facilitating its maintenance and updating. Although they have been extensively described in the Artificial Intelligence literature, their design process is at times repeated because of the lack of common software architecture and the restrictions offered by some off-the-shelf libraries and systems. This paper proposes a reusable architecture for rule-based systems described through design patterns. The aim of these patterns is to constitute a design catalog that can be used by designers to understand and create new rule-based systems, thus promoting reuse in these systems. Additionally, the use of the described patterns in the design of an intelligent tutoring system architecture is exemplified.

When carrying out tasks, police officers need up-to-date information contextualized to their current situation to support them in decision making. The results of a previous user study with the aim of capturing the information requirements of police officers have led to the implementation of a rule-based system for contextualized information delivery. In this paper, we present the overall system and discuss how the various sources of information are modelled using ontologies. Our focus is on the formalism for expressing ...

Dhundhari and Shekhawati are part of a Rajasthani language group. Shekhawati is a neighbour region of a Dhundhar. Dhundhari is a richer morphological language than Shekhwati. Both are low-resources languages. In this paper, morphology of Shekhawati and dhundhari are analysed by developing rule-based stemmer. For that,103 suffixes, 32 prefixes of Dhundhari and 89 suffixes, 32 prefixes of Shekhawati are developed. 124 rules of Dhundhari and 99 rules for Shekhawati are created. The inflectional accuracy for Dhundhari was greater than Shekhawati whereas derivational accuracy for Shekhawati was greater than Dhundhari.

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As the use of intelligent agents in critical domains increases, the need for verifying their behavior becomes stronger. Reactive rules are the main reasoning formalism for intelligent agents. For this reason, we propose the use of the OTS/CafeOBJ method for the specification of reactive rules, which will permit the verification of safety properties for reactive rule-based intelligent agents.

Work in the area of Machine Translation has been going on for several decades and it was only during the early 90s that a promising translation technology began to emerge with advanced researches in the field of Artificial Intelligence and Computational Linguistics. This held the promise of successfully developing usable Machine Translation Systems in certain well-defined domains. C-DAC took up this challenge, as we felt that India, being a multilingual and multi-cultural country with a population of approximately 950 million people and 18 constitutionally recognized languages, needs a translation system for instant transfer of information and knowledge.

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. TFR represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit fault...

Esraa Afify Modern Academy for Computer Science & Management Technology, Cairo, Egypt esraa_afify@hotmail.com Mona Nasr Faculty of Computers and Information, Helwan University, Cairo, Egypt m.nasr@helwan.edu.eg -------------------------------------------------------------------ABSTRACT--------------------------------------------------------------Student advising is an important and time-consuming effort in academic life. Academic advising has been implemented in order to fill the gap between student and the academic routine, by moving advising, complaining, evaluating, suggesting system from the traditional ways to an automated way. The researcher surveyed the existing literature; as utilized that many institutions have implemented computerized solutions in order to enhance their overall advising experience. In this paper the researcher innovates an automated mechanism for academic advising in the university system. The paper presents an overview of the development and implementatio...

This paper presents the design and implementation of a novel geosocial semantic service called "Geosocial SPLIS" (GeoSocial Semantic Personalizing Location Information Service). The service a) gets data about points of interest (POIs) and user profiles from external sources such as Google Places API and Google+, b) adopts the well known schema.org ontology, c) supports a user friendly web editor so as regular users to be able to insert and customize their daily preferences about points of interest (POIs), and POI owners their group targeted offers, d) uses RuleML and Jess rules to make this rules machine comprehensible, e) presents contextualized information on Google Maps.

Ensuring that the content of a rule-base, which is being encoded, is free from problems of consistency, completeness, and conciseness, is necessary to avoid any performance errors that might occur during consultation sessions with the rule-based system. In this paper we have described, formally, content verification of a specific type of rule-base using a digraph-based modelling approach. Through analytic formulations it is demonstrated that problems in the rule-base lead to the existence of certain properties in the digraph and various rulebase model representations that have been devised in this work. These properties, in turn, as is also shown through an example, can be examined for rule-base content verification.

Ensuring that the content of a rule-base, which is being encoded, is free from problems of consistency, completeness, and conciseness, is necessary to avoid any performance errors that might occur during consultation sessions with the rule-based system. In this paper we have described, formally, content verification of a specific type of rule-base using a digraph-based modelling approach. Through analytic formulations it is demonstrated that problems in the rule-base lead to the existence of certain properties in the digraph and various rulebase model representations that have been devised in this work. These properties, in turn, as is also shown through an example, can be examined for rule-base content verification.

1Research Scholar, JJTU 2Professor, Bharati Vidyapeeth Deemed University College of Engineering, Katraj, Pune 3Professor, Shri Jagdish Prasad Jhabarmal Tibrewala University ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Internet users around the world are growing rapidly. According to Internet live statistics, today there are more than 3 billion Internet users in the world, and the number of non-English speakers there is quite large. People with different language backgrounds could not interact with each other. This translation concept will help people communicate comfortably. Also, it will help fill the communication gap between two linguistically different grounds. It will help people in villages who have taken English language education. While most of the Indian population is not familiar with English, most of the information available on the web and in electronic information is...

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 chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a general-to-specific search which is guided by various constraints on argument dependencies and critica! terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system