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Data Mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster Analysis which belongs to the core methods of data mining is the process of discovering homogeneous groups called clusters. Given a data-set and some measure of similarity between data objects, the goal in most clustering algorithms is maximizing both the homogeneity within each cluster and the heterogeneity between different clusters. In this work, two multilevel algorithms for the clustering problem are introduced. The multilevel paradigm suggests looking at the clustering problem as a hierarchical optimization process going through different levels evolving from a coarse grain to fine grain strategy. The clustering problem is solved by first reducing the problem level by level to a coarser problem where an initial clustering is computed. The clustering of the coarser problem is mapped back level-bylevel to obtain a better clustering of the original problem by refining the intermediate different clustering obtained at various levels. A benchmark using a number of data sets collected from a variety of domains is used to compare the effectiveness of the hierarchical approach against its single-level counterpart.

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Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition. The research presents a proposal to improve the performance of single word recognition systems by an algorithm that combines more than one of the techniques used in character extraction and modulation of the neural network to study the effects of recognition science and study the effect of noise on the proposed system. In this research four systems of speech recognition were studied, the first system adopted the MFCC algorithm to extract the features. The second system adopted the PLP algorithm, while the third system was based on combining the two previous algorithms in addition to the zero-passing rate. In the fourth system, the neural network used in the differentiation process was modified and the error ratio was determined. The impact of noise on these previous systems. The outcomes were looked at regarding the rate of recognizable proof and the season of preparing the neural network for every system independently, to get a rate of distinguishing proof and quiet up to 98% utilizing the proposed framework.

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An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.

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In this paper we have aimed to diagnose skin conditions using Artificial Intelligence (AI) based classifier algorithms and do the performance analyses of those presented algorithms through confusion matrices. These algorithms are being used in a large array of different areas including medicine, and display very distinct characteristics in the sense that they are grouped under different categories such as supervised, unsupervised, statistical, or optimization. The objective of this study is to diagnose skin conditions using seven different well-known and popular as well as emerging Artificial Intelligence based algorithms and to help general practitioners and/or dermatologists develop a careful and supportive approach that leads to a probable diagnosis of skin conditions or diseases. These algorithms we chose as neural classifiers include Back- Propagation (BP), Random Forest (RF), Support Vector Machines (SVMs), Linear Vector Quantization (LVQ), Self-Organizing Maps (SOMs), Naïve Bayes, and finally Bayesian Networks. All of these algorithms have been tested and their results of diagnosing skin conditions/diseases by using data set from Dermatology Database have been compared.

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IEEE PROJECTS 2015 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. Dot Net DOTNET Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems Java Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems ECE IEEE Projects 2015 1. Matlab project 2. Ns2 project 3. Embedded project 4. Robotics project Eligibility Final Year students of 1. BSc (C.S) 2. BCA/B.E(C.S) 3. B.Tech IT 4. BE (C.S) 5. MSc (C.S) 6. MSc (IT) 7. MCA 8. MS (IT) 9. ME(ALL) 10. BE(ECE)(EEE)(E&I) TECHNOLOGY USED AND FOR TRAINING IN 1. DOT NET 2. C sharp 3. ASP 4. VB 5. SQL SERVER 6. JAVA 7. J2EE 8. STRINGS 9. ORACLE 10. VB dotNET 11. EMBEDDED 12. MAT LAB 13. LAB VIEW 14. Multi Sim CONTACT US 1 CRORE PROJECTS Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: 1croreprojects@gmail.com website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536

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UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports BN, ID, MSBN, OOBN, HBN, MEBN/PR-OWL, structure, parameter and incremental learning. The overview is presented through a slides potpourri from different presentations the Artificial Intelligence Group (GIA) from University of Brasilia (UnB) has given since 1999. It covers BN, ID, MSBN, UnBBayes Server, and MEBN. This presentation was given by Rommel Carvalho when he started his PhD at George Mason University on the Friday seminar called Krypton (http://krypton.c4i.gmu.edu/).

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Oral Defense of Doctoral Dissertation Volgenau School of Engineering, George Mason University Rommel Novaes Carvalho Bachelor of Science, University of Brasília, Brazil, 2003 Master of Science, University of Brasília, Brazil, 2008 Probabilistic Ontology: Representation and Modeling Methodology Tuesday, June 28, 2011, 2:00pm -- 4:00pm Nguyen Engineering Building, Room 4705 Committee Kathryn Laskey, Chair Paulo Costa Kuo-Chu Chang David Schum Larry Kerschberg Fabio Cozman Abstract The past few years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty in SW applications. Several approaches addressing uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides Web Ontology Language (OWL) constructs for representing Multi-Entity Bayesian Network (MEBN) theories. However, there are several important ways in which the initial version PR-OWL 1.0 fails to achieve full compatibility with OWL. Furthermore, although there is an emerging literature on ontology engineering, little guidance is available on the construction of probabilistic ontologies. This research proposes a new syntax and semantics, defined as PR-OWL 2.0, which improves compatibility between PR-OWL and OWL in two important respects. First, PR-OWL 2.0 follows the approach suggested by Poole et al. to formalizing the association between random variables from probabilistic theories with the individuals, classes and properties from ontological languages such as OWL. Second, PR-OWL 2.0 allows values of random variables to range over OWL datatypes. To address the lack of support for probabilistic ontology engineering, this research describes a new methodology for modeling probabilistic ontologies called Uncertainty Modeling Process for Semantic Technologies (UMP-ST). To better explain the methodology and to verify that it can be applied to different scenarios, this dissertation presents step-by-step constructions of two different probabilistic ontologies. One is used for identifying frauds in public procurements in Brazil and the other is used for identifying terrorist threats in the maritime domain. Both use cases demonstrate the advantages of PR-OWL 2.0 over its predecessor.

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Reasoning in very complex contexts often requires purely deductive reasoning to be supported by a variety of techniques that can cope with incomplete data. Abductive inference allows to guess information that has not been explicitly observed. Since there are many explanations for such guesses, there is the need for assigning a probability to each one. This work exploits logical abduction to produce multiple explanations consistent with a given background knowledge and defines a strategy to prioritize them using their chance of being true. Another novelty is the introduction of probabilistic integrity constraints rather than hard ones. Then we propose a strategy that learns model and parameters from data and exploits our Probabilistic Abductive Proof Procedure to classify never-seen instances. This approach has been tested on some standard datasets showing that it improves accuracy in presence of corruptions and missing data.

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What computational principles explain the success of human intelligence? I will describe recent work that combines together the unbounded flexibility of mathematical logic with the robustness of statistical inference. This combination brings us several steps closer to understanding human intelligence -- and to the tools for true intelligence engineering. Noah D. Goodman is a research scientist in the Department of Brain and Cognitive Sciences at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory. He studies the computational basis of human thought, merging behavioral experiments with formal methods from statistics and logic. He received his Ph.D. in mathematics from the University of Texas at Austin. After a brief stint as a Chicago real estate developer, he joined the Computational Cognitive Science group at MIT. Goodman has published more than thirty publications in psychology, cognitive science, artificial intelligence, and mathematics. Several of these papers have won awards.

(Reverse) Engineering Intelligence - Noah Goodman - H+ Summit @ Harvard

Humanity Plus

This presentation was given at the nectar session of the 9th Hellenic Conference on Artificial Intelligence (SETN 2016) that took place on May18th- 20th in Thessaloniki. It is about applying an agent-oriented software engineering (AOSE) methodology, i.e. the Agent Systems Engineering Methodology (ASEME) for building intelligent systems. We present it along with a case study in the Ambient Intelligence (AmI) Application Domain. We discuss the challenges, the ASEME Methodology, the System Architecture and our results.

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Today's world is full of data that is easily accessible for anyone. The problem now is how to make sense of this data and extract some useful insights from the terabytes of raw material. Typically, this involves using machine learning tools - allowing you to build classifiers, cluster data, etc. Many of these approaches give you models that describe the data accurately, but may be difficult to interpret. If you want to be able to understand the result more intuitively it is worth looking at Bayesian Networks - a graphical representation that simplifies complex mathematical model into a most likely graph of dependencies between your variables. I will talk about BNFinder - a python library allowing you to take any tabular data and convert it to a much simplified representation of conditional dependencies between variables. It can be the used for classification of unseen objects while the connection structure can be interpreted even by a non specialist. BNfinder is publicly available under GNU GPL and it can be used by anyone on their data.

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