Rough sets approach to symbolic value partition

AbstractIn data mining, searching for simple representations of knowledge is a very important issue. Attribute reduction, continuous attribute discretization and symbolic value partition are three preprocessing techniques which are used in this regard. This paper investigates the symbolic value partition technique, which divides each attribute domain of a data table into a family for disjoint subsets, and constructs a new data table with fewer attributes and smaller attribute domains. Specifically, we investigates the optimal symbolic value partition (OSVP) problem of supervised data, where the optimal metric is defined by the cardinality sum of new attribute domains. We propose the concept of partition reducts for this problem. An optimal partition reduct is the solution to the OSVP-problem. We develop a greedy algorithm to search for a suboptimal partition reduct, and analyze major properties of the proposed algorithm. Empirical studies on various datasets from the UCI library show that our algorithm effectively reduces the size of attribute domains. Furthermore, it assists in computing smaller rule sets with better coverage compared with the attribute reduction approach

Supra Soft b-Compact and Supra Soft b-Lindle÷F Spaces

The purpose of this paper is to introduce and study the concepts of supra soft b-compact and supra soft b-Lindelˆf spaces. Also we study several of their properties and characterizations in details. Furthermore, the invariance of these kinds of spaces under some types of supra soft mappings and their hereditary properties are also investigate

Some properties of soft topological spaces

AbstractFor dealing with uncertainty researchers introduced the concept of soft sets. Shabir and Naz (2011) [28], defined several basic notions on soft topology and studied many properties. In this paper, we continue investigating the properties of soft open (closed), soft nbd and soft closure. We also define and discuss the properties of soft interior, soft exterior and soft boundary which are fundamental for further research on soft topology and will strengthen the foundations of the theory of soft topological spaces

CHARACTERIZATION OF SOFT B SEPARATION AXIOMS IN SOFT BI-TOPOLOGICAL SPACES

The main aim of this article is to introduce Soft b separation axioms in soft bi topological spaces. We discuss soft b separation axioms in soft bi topological spaces with respect to ordinary point and soft points. Further study the behavior of soft regular b T3 and soft normal b T4 spaces at different angles with respect to ordinary points as well as with respect to soft points. Hereditary properties are also discussed

Methodological guidelines for reusing general ontologies

Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have

Automated identification and qualitative characterization of safety concerns reported in UAV software platforms

Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the cyber-physical nature of UAVs, software defects in these systems can cause issues with safety-critical implications. An important aspect of the lifecycle of UAV software is to minimize the possibility of harming humans or damaging properties through a continuous process of hazard identification and safety risk management. Specifically, safety-related concerns typically emerge during the operation of UAV systems, reported by end-users and developers in the form of issue reports and pull requests. However, popular UAV systems daily receive tens or hundreds of reports of varying types and quality. To help developers timely identifying and triaging safety-critical UAV issues, we (i) experiment with automated approaches (previously used for issue classification) for detecting the safety-related matters appearing in the titles and descriptions of issues and pull requests reported in UAV platforms, and (ii) propose a categorization of the main hazards and accidents discussed in such issues. Our results (i) show that shallow machine learning-based approaches can identify safety-related sentences with precision, recall, and F-measure values of about 80\%; and (ii) provide a categorization and description of the relationships between safety issue hazards and accidents

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining