Communications in Computer and Information Science
The 18 full and 13 short papers presented were carefully reviewed and selected from 255 submissions. There were organized in topical sections named: Image Processing, Pattern Analysis and Machine Vision; Information and Data Convergence; Disruptive Technologies for Future; E-Governance and Smart World
Sentiment Detection plays a vital role worldwide to measure the acceptance level of any products, movies or facts in the market. Text vectorization (converting text from human readable to machine readable format) and machine learning algorithms are widely used to detect the sentiment of users. This paper presents and evaluates a multi-level architecture based approach using stacked generalization technique named NStackSenti. The presented approach enables the combination of machine learning algorithms to improve the accuracy of detection. Here, Extremely Randomized Tree (ET), Random Forest (RF), Gradient Boost (GB), ADA Boost (ADA), Decision Tree (DT) are used as base classifiers and XGBoost classifier is used as meta estimator. The NStackSenti is applied on two separate datasets to demonstrate the effectiveness in terms of accuracy. NStackSenti provides better accuracy with trigram than unigram and bigram. It provides 83.7% and 86.24% accuracy on 2000 and 50000 data respectively.
In article features of non-uniform knowledge are considered, and the hypothesis of location on their structure is offered. This hypothesis allows to express work of the corresponding mechanism of a logic conclusion in the verbal form.
The article describes the main characterist of the scientific direction " artificial intelligens" and considered them the values that define this research area. Some ways to clarifi and formalise the bounderias of scientific research directions are suggested. Formulated conclusions, which, according to the authors, restrict the flow of scientific research in this direction.
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
This paper discusses the problem of simulation system constructing for the specific domain. Authors suggest the ontological approach using. The simulation system TriadNS is considered. This simulation system is devoted for computer networks design and analysis. Authors represent base ontology and some another ontologies describing the concepts of specific domain.
A new computer architecture named object-attribute is offered in the article. Computer of the architecture have all necessary properties for Artificial Intelligence: abstraction of data and program, height concurrency, isomorphism of data and program (i.e. possibility of painless changing of program and data structures), training and self-training of computer system, dataflow, integration of data and program, generation of object description from simple description to complex description, implementation of distribute computer system.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.