Research Paper On Artificial Intelligence Techniques In Data


Johannes Hoffart | Fabian M. Suchanek | Klaus Berberich | Gerhard Weikum

We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 447 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95% of the facts in YAGO2. In this paper, we present the extraction methodology, the integration of the spatio-temporal dimension, and our knowledge representation SPOTL, an extension of the original SPO-triple model to time and space. © 2012 Elsevier B.V. All rights reserved.


David Milne | Ian H. Witten

The online encyclopedia Wikipedia is a vast, constantly evolving tapestry of interlinked articles. For developers and researchers it represents a giant multilingual database of concepts and semantic relations, a potential resource for natural language processing and many other research areas. This paper introduces the Wikipedia Miner toolkit, an open-source software system that allows researchers and developers to integrate Wikipedia's rich semantics into their own applications. The toolkit creates databases that contain summarized versions of Wikipedia's content and structure, and includes a Java API to provide access to them. Wikipedia's articles, categories and redirects are represented as classes, and can be efficiently searched, browsed, and iterated over. Advanced features include parallelized processing of Wikipedia dumps, machine-learned semantic relatedness measures and annotation features, and XML-based web services. Wikipedia Miner is intended to be a platform for sharing data mining techniques. © 2012 Elsevier B.V. All rights reserved.


Jaume Amores

Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problem have been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e., leaving out other learning tasks such as regression). In order to perform our study, we implemented fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. © 2013 Elsevier B.V.


Sanjay Modgil | Henry Prakken

This paper builds on the recent ASPIC + formalism, to develop a general framework for argumentation with preferences. We motivate a revised definition of conflict free sets of arguments, adapt ASPIC + to accommodate a broader range of instantiating logics, and show that under some assumptions, the resulting framework satisfies key properties and rationality postulates. We then show that the generalised framework accommodates Tarskian logic instantiations extended with preferences, and then study instantiations of the framework by classical logic approaches to argumentation. We conclude by arguing that ASPIC + 's modelling of defeasible inference rules further testifies to the generality of the framework, and then examine and counter recent critiques of Dung's framework and its extensions to accommodate preferences. © 2012 Elsevier B.V. All rights reserved.


Jian Bo Yang | Dong Ling Xu

This paper aims to establish a unique Evidential Reasoning (ER) rule to combine multiple pieces of independent evidence conjunctively with weights and reliabilities. The novel concept of Weighted Belief Distribution (WBD) is proposed and extended to WBD with Reliability (WBDR) to characterise evidence in complement of Belief Distribution (BD) introduced in Dempster-Shafer (D-S) theory of evidence. The implementation of the orthogonal sum operation on WBDs and WBDRs leads to the establishment of the new ER rule. The most important property of the new ER rule is that it constitutes a generic conjunctive probabilistic reasoning process, or a generalised Bayesian inference process. It is shown that the original ER algorithm is a special case of the ER rule when the reliability of evidence is equal to its weight and the weights of all pieces of evidence are normalised. It is proven that Dempster's rule is also a special case of the ER rule when each piece of evidence is fully reliable. The ER rule completes and enhances Dempster's rule by identifying how to combine pieces of fully reliable evidence that are highly or completely conflicting through a new reliability perturbation analysis. The main properties of the ER rule are explored to facilitate its applications. Several existing rules are discussed and compared with the ER rule. Numerical and simulation studies are conducted to show the features of the ER rule. © 2013 Elsevier B.V. All rights reserved.


Ben Hachey | Will Radford | Joel Nothman | Matthew Honnibal | James R. Curran

Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms. © 2012 Elsevier B.V. All rights reserved.


Frank Hutter | Lin Xu | Holger H. Hoos | Kevin Leyton-Brown

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, and - perhaps most importantly - a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously. © 2013 Elsevier B.V.


Tim Baarslag | Katsuhide Fujita | Enrico H. Gerding | Koen Hindriks | Takayuki Ito | Nicholas R. Jennings | Catholijn Jonker | Sarit Kraus | Raz Lin | Valentin Robu | Colin R. Williams

This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the setup of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies and (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robust across different opponents, are not necessarily the ones that win the competition. Furthermore, our EGT analysis highlights the importance of considering metrics, in addition to utility maximisation (such as the size of the basin of attraction), in determining what makes a successful and robust negotiation agent for practical settings. © 2012 Elsevier B.V. All rights reserved.


Joel Nothman | Nicky Ringland | Will Radford | Tara Murphy | James R. Curran

We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify and classify names of people, locations and organisations in text. This dependence on expensive annotation is the knowledge bottleneck our work overcomes. We first classify each Wikipedia article into named entity (ne) types, training and evaluating on 7200 manually-labelled Wikipedia articles across nine languages. Our cross-lingual approach achieves up to 95% accuracy. We transform the links between articles into ne annotations by projecting the target articles classifications onto the anchor text. This approach yields reasonable annotations, but does not immediately compete with existing gold-standard data. By inferring additional links and heuristically tweaking the Wikipedia corpora, we better align our automatic annotations to gold standards. We annotate millions of words in nine languages, evaluating English, German, Spanish, Dutch and Russian Wikipedia-trained models against conll shared task data and other gold-standard corpora. Our approach outperforms other approaches to automatic ne annotation (Richman and Schone, 2008 [61], Mika et al., 2008 [46] ) competes with gold-standard training when tested on an evaluation corpus from a different source; and performs 10% better than newswire-trained models on manually-annotated Wikipedia text. © 2012 Elsevier B.V. All rights reserved.


Carlos Ansótegui | Maria Luisa Bonet | Jordi Levy

Many industrial optimization problems can be translated to MaxSAT. Although the general problem is NP hard, like SAT, many practical problems may be solved using modern MaxSAT solvers. In this paper we present several algorithms specially designed to deal with industrial or real problems. All of them are based on the idea of solving MaxSAT through successive calls to a SAT solver. We show that this SAT-based technique is efficient in solving industrial problems. In fact, all state-of-the-art MaxSAT solvers that perform well in industrial instances are based on this technique. In particular, our solvers won the 2009 partial MaxSAT and the 2011 weighted partial MaxSAT industrial categories of the MaxSAT evaluation. We prove the correctness of all our algorithms. We also present a complete experimental study comparing the performance of our algorithms with latest MaxSAT solvers. © 2013 Elsevier B.V.


David Ferrucci | Anthony Levas | Sugato Bagchi | David Gondek | Erik T. Mueller

This paper presents a vision for applying the Watson technology to health care and describes the steps needed to adapt and improve performance in a new domain. Specifically, it elaborates upon a vision for an evidence-based clinical decision support system, based on the DeepQA technology, that affords exploration of a broad range of hypotheses and their associated evidence, as well as uncovers missing information that can be used in mixed-initiative dialog. It describes the research challenges, the adaptation approach, and finally reports results on the first steps we have taken toward this goal. © 2012 Elsevier B.V.


Eduard Hovy | Roberto Navigli | Simone Paolo Ponzetto

Recent years have seen a great deal of work that exploits collaborative, semi-structured content for Artificial Intelligence (AI) and Natural Language Processing (NLP). This special issue of the Artificial Intelligence Journal presents a variety of state-of-the-art contributions, each of which illustrates the substantial impact that work on leveraging semi-structured content is having on AI and NLP as it continuously fosters new directions of cutting-edge research. We contextualize the papers collected in this special issue by providing a detailed overview of previous work on collaborative, semi-structured resources. The survey is made up of two main logical parts: in the first part, we present the main characteristics of collaborative resources that make them attractive for AI and NLP research; in the second part, we present an overview of how these features have been exploited to tackle a variety of long-standing issues in the two fields, in particular the acquisition of large amounts of machine-readable knowledge, and its application to a wide range of tasks. The overall picture shows that not only are semi-structured resources enabling a renaissance of knowledge-rich AI techniques, but also that significant advances in high-end applications that require deep understanding capabilities can be achieved by synergistically exploiting large amounts of machine-readable structured knowledge in combination with sound statistical AI and NLP techniques. © 2012 Elsevier B.V. All rights reserved.


Diego Calvanese | Giuseppe De Giacomo | Domenico Lembo | Maurizio Lenzerini | Riccardo Rosati

In this paper we study data complexity of answering conjunctive queries over description logic (DL) knowledge bases constituted by a TBox and an ABox. In particular, we are interested in characterizing the FOL-rewritability and the polynomial tractability boundaries of conjunctive query answering, depending on the expressive power of the DL used to express the knowledge base. FOL-rewritability means that query answering can be reduced to evaluating queries over the database corresponding to the ABox. Since first-order queries can be expressed in SQL, the importance of FOL-rewritability is that, when query answering enjoys this property, we can take advantage of Relational Data Base Management System (RDBMS) techniques for both representing data, i.e., ABox assertions, and answering queries via reformulation into SQL. What emerges from our complexity analysis is that the description logics of the DL-Lite family are essentially the maximal logics allowing for conjunctive query answering through standard database technology. In this sense, they are the first description logics specifically tailored for effective query answering over very large ABoxes. © 2012 Elsevier B.V. All rights reserved.


L. Giordano | V. Gliozzi | N. Olivetti | G. L. Pozzato

In this paper we propose a non-monotonic extension of the Description Logic ALC for reasoning about prototypical properties and inheritance with exceptions. The resulting logic, called ALC + T min , is built upon a previously introduced (monotonic) logic ALC + T that is obtained by adding a typicality operator T to ALC . The operator T is intended to select the "most normal" or "most typical" instances of a concept, so that knowledge bases may contain subsumption relations of the form T( C ) ⊆ D ("T(C ) is subsumed by D "), expressing that typical C -members are instances of concept D . From a knowledge representation point of view, the monotonic logic ALC + T is too weak to perform inheritance reasoning. In ALC + T min , in order to perform non-monotonic inferences, we define a "minimal model" semantics over ALC + T. The intuition is that preferred or minimal models are those that maximize typical instances of concepts. By means of ALC + T min we are able to infer defeasible properties of (explicit or implicit) individuals. We also present a tableau calculus for deciding ALC + T min entailment that allows to give a complexity upper bound for the logic, namely that query entailment is in co-NExp NP . © 2012 Elsevier B.V. All rights reserved.


Oliver Parson | Siddhartha Ghosh | Mark Weal | Alex Rogers

Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an unsupervised training method for non-intrusive monitoring which, unlike existing supervised approaches, does not require training data to be collected by sub-metering individual appliances, nor does it require appliances to be manually labelled for the households in which disaggregation is performed. Instead, we propose an approach which combines a one-off supervised learning process over existing labelled appliance data sets, with an unsupervised learning method over unlabelled household aggregate data. First, we propose an approach which uses the Tracebase data set to build probabilistic appliance models which generalise to previously unseen households, which we empirically evaluate through cross validation. Second, we use the Reference Energy Disaggregation Data set to evaluate the accuracy with which these general models can be tuned to the appliances within a specific household using only aggregate data. Our empirical evaluation demonstrates that general appliance models can be constructed using data from only a small number of appliances (typically 3-6 appliances), and furthermore that 28-99% of the remaining behaviour which is specific to a single household can be learned using only aggregate data from existing smart meters. © 2014 Elsevier B.V.


Wolfgang Dvořák | Matti Järvisalo | Johannes Peter Wallner | Stefan Woltran

Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the area of Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semi-stable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems. © 2013 Elsevier B.V.


Francesco Calimeri | Martin Gebser | Marco Maratea | Francesco Ricca

© 2015 Elsevier B.V. All rights reserved. Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and non-monotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely related problem solving areas such as Boolean Satisfiability, Satisfiability Modulo Theories, Quantified Boolean Formulas, Planning, etc. This paper reports about the fifth edition of the ASP Competition by covering all aspects of the event, ranging from the new design of the competition to an in-depth analysis of the results. The paper comprises also additional analyses that were conceived for measuring the progress of the state of the art, as well as for studying aspects orthogonal to solving technology, such as the effects of modeling. A detailed picture of the progress of the state of the art in ASP solving is drawn, and the ASP Competition is located in the spectrum of related events.


Pierre Baldi | Peter Sadowski

Dropout is a recently introduced algorithm for training neural networks by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. © 2014 The Authors.


Günther Charwat | Wolfgang Dvořák | Sarah A. Gaggl | Johannes P. Wallner | Stefan Woltran

© 2015 The Authors. Published by Elsevier B.V. Within the last decade, abstract argumentation has emerged as a central field in Artificial Intelligence. Besides providing a core formalism for many advanced argumentation systems, abstract argumentation has also served to capture several non-monotonic logics and other AI related principles. Although the idea of abstract argumentation is appealingly simple, several reasoning problems in this formalism exhibit high computational complexity. This calls for advanced techniques when it comes to implementation issues, a challenge which has been recently faced from different angles. In this survey, we give an overview on different methods for solving reasoning problems in abstract argumentation and compare their particular features. Moreover, we highlight available state-of-the-art systems for abstract argumentation, which put these methods to practice.


Malik Ghallab | Dana Nau | Paolo Traverso

Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actor's deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration. © 2013 Elsevier B.V.

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artificial intelligence research papers 2015




Artificial Intelligence and its Application in Different Areas
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Ethics of artificial intelligence
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Weighted Logics for Artificial Intelligence
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Artificial Intelligence, Big Data, and Cancer
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Predicting burned areas of for-est fires: an artificial intelligence approach
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AI-Board-An Artificial Intelligence Problem Resolution and Learning System
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Artificial Intelligence Collusion: When Computers Inhibit Competition
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Artificial Intelligence and Pro-Social Behaviour
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AIBIRDS: The Angry Birds Artificial Intelligence Competition
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Evaluation of Machine Learning Algorithms in Artificial Intelligence
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Artificial Intelligence Driven Judgment Card Game
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1 Metallic interfaces are a key ingredient in controlling the strength, ductility, reliability and lifetime properties of metal-based structural and functional materials and devices. This holds for bulk materials, where many properties are controlled by the behaviour at and through

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ABSTRACT Artificial intelligence (AI) is one of the oldest and best known research fields in computer science which is aimed at giving intelligence in machines. In spite of enormous effort geared towards AI, its boundary and interference to other fields are yet undefined.

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Abstract- A new type of floating breakwaters (FBs) is introduced by changing the longitudinal section of p-shaped FBs into a stepwise shape. The hydrodynamic performance of so-called step floating breakwaters (SFBs) is evaluated through 144 experiments on SFBs of

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Abstract:Cancer has been one of the most famous conditions discussed and researched about throughout the human history. Some of the earliest medical records regarding cancer are dated back to around 1600 BC. Cancer is a general condition which is subdivided into

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Abstract Several civil engineering activities need dynamic time history analysis or other numerical simulations. In such cases, it is very important to have accurate and adequate accelerograms. However in most situations, there is not enough data for a specific site or

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The academic staff consists of the scientists and specialists of the highest qualifications, 1675 academic teachers are employed at the West Pomeranian University of Technology, of which 906 are Professors and Doctors. WPUT participates in various European

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Abstract Following an introduction to the context of Human-Level Artificial Intelligence (HLAI) and (computational) analogy research, a formal analysis assessing and qualifying the suitability of the Heuristic-Driven Theory Projection (HDTP) analogy-making framework for

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ABSTRACT As name implies artificial intelligence is making the machines like human that can think like human and can do the work like human. Now a days' in every step of our life we are using artificial intelligence like when we use Google and another shopping sites

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PHILOSOPHICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCE A course for second year AI students
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Margaret Boden (Ed) The Philosophy of ArtificialIntelligence (ed.) (Oxford University Press,) 1990. Churchland, PM (1984). 5 Page 6. Haugeland, John, (ed) Mind Design: Philosophy, Psychology, ArtificialIntelligence, Bradford Books, MIT Press, 1981.

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Abstract: In recent years, social, economic and fiscal factors have produced strong modifications of the Italian real estate market, that currently appears as a complex system characterized by continuous transformation. In this context, for real estate operators the

EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE
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Abstract In unsupervised learning or clustering the aim is to discover groups of similar instances within the data. In this approach, we have no information about the class label of data or how many classes there are. Information extraction from unstructured,

Artificial Intelligence for Lithology Identification through Real-Time Drilling Data
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Abstract In order to reduce drilling problems such as loss of circulation and kick, and to increase drilling rate, bit optimization and shale swelling prohibition, it is important to predict formation type and lithology in a well before drilling or at least during drilling. Although

Forecasting of Ozone Episodes through statistical and artificial intelligence based models over Delhi metropolitan area
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Abstract:-Ozone is the one of the most phytotoxic air pollutants, and causes considerable damage to ecological system throughout the world. Urban regions tend to have maximum ozone values in the late afternoon and minimum values in the early morning hours. The 8-

HEALTH DATA ENTANGLEMENT AND ARTIFICIAL INTELLIGENCE-BASED ANALYSES TO IMPROVE THE EFFECTIVENESS OF SERVICES AND TACKLE
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CONCLUSION The burden of healthcare costs will continue to grow unless and until the efficiency and efficacy of healthcare systems will be achieved. HDE and AI-based analyses can be adopted to improve the effectiveness of health governance system in ways that

Artificial Intelligence Based Robot Control Using Face and Hand Gesture Recognition: A Review
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Abstract In this Paper, we are presenting a review for the interaction to robot for control its operation with the help of Artificial intelligence techniques. We are gone through the many research papers and article for review, our many focus for the robot control mechanism

Artificial Intelligence Aided Recommendation Based Mobile Trip Planner For Eskisehir City
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Abstract:Recent years have seen a proliferation of applications aimed for the mobile users. Although there are some mobile trip planning applications available for big cities such Istanbul, they lack some important features that would be necessary for the best trip quality

Artificial Intelligence in the Concertgebouw
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Abstract In this paper we present a real-world application (the first of its kind) of machine listening in the context of a live concert in a world-famous concert hall–the Concertgebouw in Amsterdam. A real-time music tracking algorithm listens to the Royal Concertgebouw

Improving WSN Routing and Security with an Artificial Intelligence approach
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Abstract. Wireless Sensor Network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment, and organizing the collected data at a central location. Research in WSNs is gaining

SOME PHILOSOPHICAL ASPECTS OF ARTIFICIAL INTELLIGENCE
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Plato defined intelligence as a matter that distinguishes a human being from an animal. In German classical philosophy (Kant, Hegel) the term intelligence (Verstand) is used to define the human ability to form concepts. Further, intelligence is regarded as congenital or

Education still needs Artificial Intelligence to support Personalized Motor Skill Learning: Aikido as a case study
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Abstract. Motor skill learning is hardly considered in current AIED literature. However, there are many learning tasks that require consolidating motor tasks into memory through repetition towards accurate movements, such as learning to write, to draw, to play a

The Use of Artificial Intelligence In Selecting Stocks (With Emphasis On Specific Industry
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Mehdi Fallahdost 1*, Seyed Mohammad Termeh Ghaziani 2, Mohammadreza Nemati 3 sually, investors and investment funds in the stock market, Interested in buying shares of companies with superior investment returns and growth are appropriate, to cover the

How a Middle School Teacher Can Use Artificial Intelligence to Teach Evolution
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Synopsis: This curriculum unit (CU) parallels the concepts of Evolution and Artificial Intelligence (AI) for the purpose of making the Theory of Evolution easier to grasp for the middle school student. This analogy and background information is supplied for the

SCIENCES RESEARCH TECHNOLOGY SWARM INTELLIGENCE TECHNIQUE ANDARTIFICIAL INTELLIGENCE TECHNIQUE IN SOFTWARE TESTING (
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In this research work, we have used genetic algorithm an artificial intelligence technique for automatic generation of test data for integer in case of path testing and for automatic generation of test cases. In case of small problems paths can be easily seen and we can

PALAIS: A 3D Simulation Environment for Artificial Intelligence in Games
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Abstract. In this paper we present PALAIS a virtual simulation environment for Artificial Intelligence (AI) in games. The environment provides functionality for prototyping, testing, visualisation and evaluation of game AI. It allows definition and execution of arbitrary,

A REVIEW OF ARTIFICIAL INTELLIGENCE BASED BUILDING ENERGY PREDICTION WITH A FOCUS ON ENSEMBLE PREDICTION MODELS
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ABSTRACT Building energy usage prediction plays an important role in building energy management and conservation. Building energy prediction contributes significantly in global energy saving as it can help us to evaluate the building energy efficiency; to conduct

Comparison of Stochastic Modelling With Artificial Intelligence Based
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Abstract: Accurate load forecasting is very important for electric utilities in planning for new plants. Also it is very significant for the routine of maintaining, scheduling daily, electrical generation, and loads. In this study, emphasis was considered on short-term load

Artificial Intelligence Module 2 Embedding and Manifold Learning
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Page 1. ArtificialIntelligence Module 2 Embedding and Manifold Learning Andrea Torsello Page 2. Multidimensional Scaling (MDS) It is often easier to provide distances What can we do if we only have distances Multidimensional scaling

Car Tyre Replacement Robot Using Artificial Intelligence
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Abstract:The aim of this paper work is to replace defected tyres in cars using robot. This robot is a package of removal and replacement process involved in tyres. This robot is implemented by artificial intelligence and image processing techniques totally powered by

ARTIFICIAL INTELLIGENCE AND MEDICAL SCIENCE: A SURVEY
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Abstract:Tech titans like Google, Amazon, Microsoft, and Apple already have made huge investments in artificial intelligence to deliver tailored search results and build virtual personal assistants. Now, that approach is starting to trickle down into health care, thanks

Artificial Intelligence Role in Cybersecurity Infrastructures
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Abstract The information technology domain advances and at the same time criminals are using new methods to commit cybercrimes. Cyber infrastructures are vulnerable to threats and other intrusions. Physical or virtual appliances and the human intervention are not

Artificial Intelligence in Robot Path Planning
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Abstract: Mobile robot path planning problem is an important combinational content of artificial intelligence and robotics. Its mission is to be independently movement from the target point make robots in their work environment while satisfying

Implementation of a course in artificial intelligence and expert systems on top of a distance-learning platform
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Abstract In the current paper, presented is animplementation of a distance-learning course on the subject of Artificial Intelligence and Expert Systems for students pursuing bachelor's degree in the field of Informatics and Information Technologies. The distance-learning

INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE AND INTERACTIVE MULTIMEDIA
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-COMMERCE customers are used to being guided by some type of e-assistant which helps them with information overload. Recommendation web-engines assist the user in a variety of e-commerce applications, such as those for buying music, books and mobile phones.

POSITION TRACKING SYSTEM TO FIND SHORTEST PATH TO OBJECT USING ARTIFICIAL INTELLIGENCE FUZZY LOGIC
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ABSTRACT: To find the shortest distance always suffers struggle and more complexity rather than simple and easy path. Here we are finding the position shortest path to an object from image taken from camera or any visual sensing device by making use of fuzzy logic

Humanity's Attempt to Create Public Policies to Monitor Advancements in Artificial Intelligence Design
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Synopsis: This unit examines the historical impacts of artificial intelligence (AI) advancements from Turing's Imitation Game through today's AI achievements, such as Google's self-driving car. Students will research how AI advancements have impacted the

THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN MACHINERY
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Summary The paper presents the cardinal usage of the methods of artificial intelligence in technical diagnostics. Chosen systems were attributed a special expert part, helping the process of diagnostic inference. Logging to expert knowledge systems is often possible for

A New Investigation about the Artificial Intelligence in Reproducing Kernel Hilbert Spaces: An Analytical Study
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Abstract:The current paper aims to compute the artificialintelligence for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression. 1 Introduction The artificialintelligence of real-valued

SIMULATION APPROACH OF CUTTING TOOL MOVEMENT USING ARTIFICIAL INTELLIGENCEMETHOD
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Abstract In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be

A COMPARATIVE PERFORMANCE ANALYSIS OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR INDUCTION MOTOR DRIVE
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Abstract: The paper presents various Artificial Intelligent controllers like ANN, Fuzzy controller, ANFIS, Fuzzy Neural Controller applied to induction motor drive system. Artificial Intelligent Controller (AIC) could be the best controller for Induction Motor control. This is

Artificial Intelligence through the eyes of Organised Sound
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Abstract Artificial intelligence is a rich and still-developing field with many musical applications. This article surveys the use of artificial intelligence approaches in the pages of Organised Sound, from the first issue to the present day. Often, these approaches are

A novel correlation approach to predict total formation volume factor, using artificial intelligence
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This paper presents a new correlation approach to predict total formation volume factor below the bubble point pressure for oil and gas mixtures. This correlation is obtained by using more than 450 experimental data points which are collected from samples of Iranian

Survey on Heuristic Search Techniques to Solve Artificial Intelligence Problems
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Abstract: Artificial intelligence (AI) is an area of computer science that highlights the creation of machines that are intelligent, also they work and react like humans. Since AI problems are complex and cannot be solved with direct techniques we resort to heuristic search

Authentic Modeling of Complex Dynamics of Biological Systems by the Manipulation ofArtificial Intelligence
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Abstract:The recent meteoric significant developments in the biological and medical sciences have been the culmination of substantial efforts devoted to precisely modeling the behavior of biological systems and their responses to various stimuli. The complicated

Comparative Analysis on the Performance of Artificial Intelligence (AI) Classification Algorithms
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Abstract: There is range of AI Algorithms used in data mining to determine the hidden or unknown information in a datasets. AI techniques are wide and too many to mention and most of these techniques have their own subfields. This paper determines the accuracy

RISK DETERMINED OF MOTOR OWN DAMAGE INSURANCE BY POLICIES USING ARTIFICIAL INTELLIGENCE
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ABSTRACT: As a result of the increase of people's living standards, the number of vehicles has increased. The increasing number of vehicles has led to an increase in traffic density. Thus, an increased risk of accident and motor own damage insurance has led to their

Use of Artificial Intelligence in Software Development Life Cycle: A state of the Art Review
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Abstract:Artificial Intelligence (AI) is the younger field in computer science ready to accept challenges. Software engineering (SE) is the dominating industrial field. So, automating SE is the most relevant challenge today. AI has the capacity to empower SE in that way. Here

Artificial intelligence based method for optimal design of harmonic filters
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Abstract-Conventional approaches for the design of passive power filter (PPF) depend on experiences and single technology criterions which are difficult to achieve the optimal solution. In this paper a Simplified Adaptive Step Length Bacterial Foraging algorithm (S-

Renewable Energy Sources (RES) Utilization and Adaptation Technologies using Artificial Intelligence Expert Systems to Support and Secure RES Projects,
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Abstract: Selecting a renewable energy oriented technology in order to support an investment plan or a geo-political energy analysis or policy, is a quite complex and multi- parametrical process. The technical and operational differentiation among the energy

Testing of Various Embedded System with Artificial Intelligence Approach
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Abstract:Testing of Embedded System is a great challenge for software testers. Testing of embedded systems is most sophisticated and time consuming task because of its different infrastructure, organizations and techniques used for its development. Testing of

Modelling Cultural, Religious and Political Affiliation in Artificial Intelligence Decision-Making
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Abstract. This paper examines cutting-edge work in the generation of individual AI actors who behave according to procedurally-generated social, cultural, political and religious norms. Based on the author's ongoing development of the game Ultima Ratio Regum (

Algorithms, Artificial Intelligence, SOFT Computing and Informatics
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An Event Polarized Paradigm for ADL Detection in AAL Context

Application of Artificial Intelligence to Minimize Operating Costs of Smart Grid Energy Sources
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Abstract This paper formulates a unit commitment optimization problem for renewable and combined energy sources distributed in a smart grid. Also we present two experiments. The first experiment consists of cluster analysis of the daily diagrams of electric energy-

Artificial Intelligence in Network Intrusion Detection
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Abstract In the beginning of the Internet era detection of network attacks has been almost solely done by human operators. They anticipated network anomalies in front of consoles, where based on their expert knowledge applied necessary security measures. With the

Artificial Intelligence Based Speed Control of Induction Motor-A Detailed Study
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Abstract-This paper presents a novel speed vector control scheme of an induction motor (IM) based on robust adaptive variable structure control (VSC) law and its experimental validation are presented. The design of the speed controller greatly affects the

SHORTEST PATH TO OBJECT FOR REAL TIME SYSTEM USING ARTIFICIAL INTELLIGENCE FUZZY LOGIC
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ABSTRACT: To find the shortest distance always suffers struggle and more complexity rather than simple and easy path. Here we are finding the position shortest path to an object from image taken from camera or any visual sensing device by making use of fuzzy logic

Implementing Artificial Intelligence based DBN Models: An Experimental Analysis
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Abstract The Deep belief networks (DBNs) are found to be the most prominent techniques, which are being used in natural language understanding. Natural language understanding has become one of the greatest priorities for businesses, particularly for call centers and

The presentation of the set of stock exchanges features in international portfolio diversification for the application of artificial intelligence
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Summary The main aim of this article is to present the characteristics of stock exchanges, which may allow for an assessment of their attractiveness for investment funds in equities. In this publication the author also made presentations of major stock exchanges in the world.

CS365A-Introduction to Artificial Intelligence
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Abstract Automobile companies now a days are facing a lot of competition in the field of meeting the requirements of the large variety of consumers. There is a need of a mechanism that automatically generates feedback for every new development made in the industry. In

A Novel Approach to Reproduced Kernel Hilbert Space for Artificial Intelligence Control: Using Monte-Carlo Estimates of Operators Arising
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Abstract:An embedding of stochastic optimal control problems of artificial intelligence form into reproducing kernel Hilbert spaces is presented in this study. A model-free, non- parametric approach for calculation of an approximate solution to the control problem is

Artificial Intelligence and the Brain: Creating a Super-human
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Synopsis: We have learned a great deal about the human brain through modern science, but much of the human brain remains a mystery. We often think of our species as the most advanced in the animal kingdom. But what if there was a way to enhance our brain

Artificial Intelligence in the Design of Microstrip Antenna
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Abstract This paper presents a Neural Network model for the design of Microstrip Antenna for a desired frequency between 3.5 GHz to 5.5 GHz. The results obtained from the proposed method are compared with the results of IE3D and are found to be in good The 28th Canadian Conference on Artificial Intelligence (AI 2015) built on a long sequence of successful conferences, bringing together Canadian and international researchers, presenting and discussing original research. The conference was held in Halifax, Nova

MODELING THE CEREAL DRYER USING GEOTHERMAL WATER AS HEATING AGENT BY APPLYING ARTIFICIAL INTELLIGENCE AS AN AUTOMATIC
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Abstract This work has in view to model the guidance process of drying the cereals from the dryers using thermal water as a heating agent and appealing to the utilisation of guidance systems which use artificial intelligence through Fuzzy controllers. The temperature of the

Explorative Artificial Bee Colony Algorithm: A Novel Swarm Intelligence Based Algorithm for Continuous Function Optimization
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Abstract: The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully employed to numerous scientific and engineering problems. However, ABC sometimes suffers from premature

Old papers


Artificial Intelligence apply for prediction of Laser Cutting Process–A review
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ABSTRACT There are many factors effective on performance of the laser cutting process. Identification of more effective factors requires which will give the significant effect on the cutting quality of materials. In recent years the researchers have explored the number of

Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis
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ABSTRACT This paper presents an intelligent fault classification approach for power transformer dissolved gas analysis (DGA). Fault diagnosis methods by the DGA and artificial intelligence (AI) techniques are implemented to improve the interpretation accuracy for

Human-Level Artificial Intelligence Must Be an Extraordinary Science
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ABSTRACT Aiming to create a cognitive system with human-level intelligence is as different from normal scientific objectives as reaching artificial immortality is to the goals of modern medicine. Most researchers in artificial intelligence, along with the institutions that support

Ontologies, Knowledge Representation, Artificial Intelligence–Hype or Prerequisites for Interoperability?
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ABSTRACT Nowadays, eHealth and pHealth solutions have to meet advanced interoperability challenges. Enabling pervasive computing and even autonomic computing, pHealth system architectures cover many domains, scientifically managed by specialized disciplines using

Rationally-shaped artificial intelligence
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ABSTRACT Systems with the computational power of the human brain are likely to be cheap and ubiquitous within the next few decades. As technology becomes more intelligent, we need to ensure that it remains safe and beneficial. This paper describes a rational

An Overview of Artificial Intelligence based Pattern Matching in a Security and Digital Forensic context
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Abstract Many real world security and digital forensics tasks involve the analysis of large amounts of data and the need to be able to classify parts of that data into sets which are not well or even easily defined. Rule based systems can work well and efficiently for simple

Convergence of Artificial Intelligence, Emotional Intelligence, Neural Network and Evolutionary Computing
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ABSTRACT This paper presents a new perspective of Artificial Intelligence (AI). Although, number of attempts has been made to make an artifact intelligent, including evolution theory, neural network etc and a number of problems have been solved using these concepts but 

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