IJARAI Volume 3 Issue 9

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Paper 1: An Inference Mechanism Framework for Association Rule Mining

Abstract: Available approaches for Association Rule Mining (ARM) generates a large number of association rules, these rules may be trivial and redundant and also such rules are difficult to manage and understand for the users. If we consider their complexity, then it consumes lots of time and memory. Sometimes decision making is impossible for such kinds of association rules. An inference approach is required to resolve this kind of problem and to produce an interesting knowledge for the user. In this paper, we present an inference mechanism framework for ARM, which would be capable enough for resolving such problems, it would also predict future possibilities using Markov predictor by analyzing available fact and inference rules.

Author 1: Kapil Chaturvedi

Author 2: Dr. Ravindra Patel

Author 3: Dr. D.K. Swami

Keywords: Inference rules; ARM; Knowledgebase; Expert System

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Paper 2: Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

Abstract: With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

Author 1: Mr.D. V. Kodavade

Author 2: Dr. Mrs.S.D.Apte

Keywords: Expert Systems; fuzzy; Inference; Knowledge base

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Paper 3: Design and Implementation of Rough Set Algorithms on FPGA: A Survey

Abstract: Rough set theory, developed by Z. Pawlak, is a powerful soft computing tool for extracting meaningful patterns from vague, imprecise, inconsistent and large chunk of data. It classifies the given knowledge base approximately into suitable decision classes by removing irrelevant and redundant data using attribute reduction algorithm. Conventional Rough set information processing like discovering data dependencies, data reduction, and approximate set classification involves the use of software running on general purpose processor. Since last decade, researchers have started exploring the feasibility of these algorithms on FPGA. The algorithms implemented on a conventional processor using any standard software routine offers high flexibility but the performance deteriorates while handling larger real time databases. With the tremendous growth in FPGA, a new area of research has boomed up. FPGA offers a promising solution in terms of speed, power and cost and researchers have proved the benefits of mapping rough set algorithms on FGPA. In this paper, a survey on hardware implementation of rough set algorithms by various researchers is elaborated.

Author 1: Kanchan Shailendra Tiwari

Author 2: Ashwin. G. Kothari

Keywords: Rough set theory; Discernibility matrix; reduct; Core; FPGA; classification

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Paper 4: Dynamic Programming Method Applied in Vietnamese Word Segmentation Based on Mutual Information among Syllables

Abstract: Vietnamese word segmentation is an important step in Vietnamese natural language processing such as text categorization, text summary, and automated machine translation. The problem with Vietnamese word segmentation is complicated because Vietnamese words are not always separated by a space. One word can include one or more syllables depending on the context. This paper proposes a method for Vietnamese word segmentation based on the mutual information among the syllables combined with dynamic programming. With this method, we can achieve an accuracy rate of about 90% with a raw text corpus.

Author 1: Nguyen Thi Uyen

Author 2: Tran Xuan Sang

Keywords: Vietnamese word segmentation; dynamic programming; mutual information; Vietnamese syllables

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