Mining association RULES in DISTRIBUTED databases is an interesting problem in the context of parallel and DISTRIBUTED data mining. A number of approaches have, so far, been proposed for DISTRIBUTED mining of association RULES. However, most of them consider all types of frequent itemsets the same, even though there are different types of itemsets in DISTRIBUTED databases, e.g., derivable and non-derivable. In this study, a new application of DEDUCTION RULES is introduced for DISTRIBUTED mining of association RULES which exploits the derivability of itemsets to reduce communication overhead and to enhance response time. A new algorithm is proposed which mines derivable and non-derivable frequent itemsets in a DISTRIBUTED database. Since the collection of derivable and non-derivable frequent itemsets form all frequent itemsets, our algorithm mines all frequent itemsets rather than a subset of them. In the algorithm, there is no need to scan local databases and exchange messages in order to obtain support counts of derivable frequent itemsets, since each site can produce them autonomously. Experimental evaluations on horizontally partitioned real-life datasets show that such exploitation drastically reduces communication and also improves response time.  Therefore the new algorithm is useful when communication bandwidth is the main bottleneck.

Intelligent agents are considered as significant means towards realizing the semantic web vision. On the Semantic Web, integrating ontologies and RULES enables software agents to interoperate between them, however, this leads to a problem, that no studies have focused on effective DISTRIBUTED reasoning for integrating ontologies and RULES in multiple knowledge-bases. The methods that have been presented for DISTRIBUTED reasoning not only get a lot of times and memory, but also do not lead to a complete and sound reasoning. In this paper, to solve this problem, we present a DISTRIBUTED reasoning system that deals with the representation of the knowledge-base of order sorted logic. This logic is able to describe the hierarchy of predicates and inheritance of expressions that there are in our natural language. To have a DISTRIBUTED reasoning, our proposed method uses the expansion of rigid and valid-non-rigid properties between knowledge-bases. Furthermore, with considering time and the situation of properties for reasoning, the non-rigid properties have not been ignored, in fact, in their valid time and situation, they are used. With this method, we achieve a complete reasoning and, moreover, the extracted knowledge is completely considered in the knowledge-bases and we have a DISTRIBUTED reasoning with high efficiency and sound without missing any information.

The most important topic in logic is DEDUCTION which is treated in logical books under the heading of argument. Ancients divided argument into the three classes of syllogism, induction and analogy.As to kinds of and the sort of division of argument some questions has been raised. On the one hand, besides the theory of syllogism which is without doubt Aristotle's masterpiece one of important works of Aristotle's in the field of logic is the topic of "conversion", "contradiction" and "contrast".Of course, in Organon these topics have been treated incidentally. Ibn Sina added some relations to those which Aristotle had introduced and by finding the similarity of these topics brought them together in his logical works.After Ibn sina, logicians by following him introduced these relations under the heading "representative argument". The same topic has been treated in recent books of traditional logic with the title "RULES of proposition". This title suggests that these logicians regard "RULES of proposition" as a kind of DEDUCTION and argument.In this article the author defends the latter view and by assuming the conversion to be a kind of argument and examples like that has proposed a division of DEDUCTION in which the place of these arguments has been clearly defined and has no difficulties of previous division.

The theory of DISTRIBUTED detection is receiving a lot of attention. A common assumption used in previous studies the conditional independence of the observation. In this paper, the optimization of local DISTRIBUTED detection network with fixed fusion RULES to develop a numeric algorithm based on neyman-pearson criterion. Simulation results are presented to demonstrate the efficiency and convergence properties of the algorithm.

The purpose of this study is to describe content analysis. The paradigm followed in this article is inductive and deductive reasoning. The researcher has adopted a descriptive content analysis. The writer while trying to describe the distinction between qualitative and quantitative analysis, make an attempt to study the role of inductive and deductive reasoning in qualitative content analysis. The purpose of this analysis is to present and create important themes in the body of content. Also a rich description of the text as a social reality is offered. In religious text analysis, the roots are analyzed in order to build a model for conceptual form and propositions are provided as a means of religious thought. The roots analysis has a structural then logical nature and is hidden within the meaning created by the text. In content analysis, in addition to the text the features of message as well source of message and the sender of massage are analyzed to achieve a reasonable result. In this article, the process of qualitative content analysis are studied. The above research method is recommended to specialists who are interested in interdisciplinary research activities in the field of psychology and educational management in conjunction with Islamic study.

DISTRIBUTED association RULES mining is one of the most important data mining methods that extracts the inter dependence of data items from decentralized data sources, regardless of their physical location and is based on the process of extracting repeated items. When exploration algorithms are implemented on large-scale data, a large number of recurring items are produced, many of which are irrelevant, ambiguous, and unusable for the business, thus causing a challenge called "combination explosion ". In this paper, a new coalition method based on DISTRIBUTED data mining and domain archeology, abbreviated to DARMASO, is proposed to address this challenge. This method uses three algorithms: the DARMASOMAIN algorithm to guide and control the process of exploration and aggregation of universal RULES, the DARMASOPRU algorithm to reduce and prune the data and the DARMASOINT algorithm to explore and aggregate the RULES of all the generated data sources. DARMASO uses a map-reduce-based DISTRIBUTED computational model in a multi-agent DISTRIBUTED environment. It also provides a practical way for semantic mining of large-scale data sets. This method filters out the association RULES of generality based on the purposes of data mining as well as the needs of the user and only produces and maintains useful RULES. Reducing the scope of exploration and filtration of RULES is achieved through the process of semantic pruning in the form of removing inappropriate candidates from the set of frequent items and producing association RULES of utility. The implementation is performed using a data set from the scope of natural disasters and the earthquake class. It also improves the speed and quality of rule extraction and generates practical, reliable, logical, quality and valuable RULES to support decision-making amid the masses of data.

Rebeca is an actor-based language for modeling concurrent and DISTRIBUTED systems, Its Java-like syntax makes it easy-to-use for practitioners and its formal foundation is a basis to make different formal verification approaches applicable. Compositional verification and abstraction techniques are used in formal verification of Rebeca models to overcome state explosion problems, The main contribution of this paper is to show how model checking and DEDUCTION are integrated for verifying certain properties of these models. DEDUCTION is used to prove that abstraction techniques preserve a set of behavioral specifications in temporal logic and is also used in applying the compositional verification approach, on the basis of the model checked components.

Subintuitionistic logics as a theme were first studied by G. Corsi, who introduced a basic system F in a Hilbert style proof system. The system F is sound and complete with respect to the class of Kripke frames in which the assumption of preservation of truth is dropped and which are not assumed to be reflexive or transitive. Dick de Jongh and F. Sh. Maleki, have introduced a basic logic WF in a Hilbert style proof system, much weaker than F. They proved that subintuitionistic logic WF is sound and complete with respect to the class of neighborhood models with a somewhat more complex definition than the neighborhood models for classical (non-normal) modal logics. So far, no natural DEDUCTION system has been presented for any of these two basic systems F and WF. This paper is devoted to the introduction of natural DEDUCTION systems for subintuitionistic logics WF and F.

Aristotelian DEDUCTION RULES, which are usually considered as “THE RULES OF THE CATEGORICAL SYLLOGISM” in the elementary logic text books, are proper tools which help beginners in logic to examine the validity of a categorical syllogism. Authors of Persian logic text books, influenced by the authors of English logic text books, rewrite these RULES with only some minor changes and revisions in their books and apply them for the same aim. These revisions depend on many different factors including the authors’ personal interests and those English logic text books which were his main reference. The main aim of this article in the first step is to provide an analytical method for formalizing the RULES of DEDUCTION which can lead us to find a mechanical and algorithmic method and in the next step, is to follow this computable and formal approach to analyze and criticize the DEDUCTION RULES in Persian logic text books. In addition to categorizing “The RULES of the categorical syllogism”, we will propose a new version of such formalized RULES by appealing to the concept of “distribution”.