What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Secure data sharing and processing in heterogeneous clouds

The extensive cloud adoption among the European Public Sector Players empowered them to own and operate a range of cloud infrastructures. These deployments vary both in the size and capabilities, as well as in the range of employed technologies and processes. The public sector, however, lacks the necessary technology to enable effective, interoperable and secure integration of a multitude of its computing clouds and services. In this work we focus on the federation of private clouds and the approaches that enable secure data sharing and processing among the collaborating infrastructures and services of public entities. We investigate the aspects of access control, data and security policy languages, as well as cryptographic approaches that enable fine-grained security and data processing in semi-trusted environments. We identify the main challenges and frame the future work that serve as an enabler of interoperability among heterogeneous infrastructures and services. Our goal is to enable both security and legal conformance as well as to facilitate transparency, privacy and effectivity of private cloud federations for the public sector needs. © 2015 The Authors

Pets that learn

Thesis (M.S.V.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1989.Includes bibliographical references (leaves 41-43).by William H. Coderre.M.S.V.S

Multi-Attribute Dispatching Rules For Agv Systems With Many Vehicles

Internal transport systems using automated guided vehicles (AGVs) are widely used in many facilities such as warehouses, distribution centers and transshipment terminals. Most AGV systems use online dispatching rules to control vehicle movements. In literature, there are many types of dispatching rules such as single- and multi-attribute dispatching rules. However, a dispatching rule that is good for all cases does not exist. In this research, we study a specific type of AGV environments which has not received much attention from researchers - AGV systems with many vehicles as can be seen in airport baggage handling systems. We propose two new multi-attribute dispatching rules for this type of environment and compare their performance with that of two of the best dispatching rules in literature. Using simulation we show that the new multi-attribute dispatching rules are robust and perform very well

Explaining and Refining Decision-Theoretic Choices

As the need to make complex choices among competing alternative actions is ubiquitous, the reasoning machinery of many intelligent systems will include an explicit model for making choices. Decision analysis is particularly useful for modelling such choices, and its potential use in intelligent systems motivates the construction of facilities for automatically explaining decision-theoretic choices and for helping users to incrementally refine the knowledge underlying them. The proposed thesis addresses the problem of providing such facilities. Specifically, we propose the construction of a domain-independent facility called UTIL, for explaining and refining a restricted but widely applicable decision-theoretic model called the additive multi-attribute value model. In this proposal we motivate the task, address the related issues, and present preliminary solutions in the context of examples from the domain of intelligent process control

Development of an Efficient Planned Maintenance Framework for Marine and Offshore Machinery Operating under Highly Uncertain Environment

The constantly increasing complexity of marine and offshore machinery is a consequence of a constant improvement in ship powering, automation, specialisation in cargo transport, new ship types, as well as an effort to make the sea transport more economic. Therefore, the criteria of reliability, availability and maintainability have become very important factors in the process of marine machinery design, operation and maintenance. An important finding from the literature exposed that failure to marine machinery can cause both direct and indirect economic damage with a long-term financial consequence. Notably, many cases of machinery failures reported in databases were as a result of near misses and incidents which are potential accident indicators. Moreover, experience has shown that modelling of past accident events and scenarios can provide insights into how a machinery failure can be subsisted even if it is not avoidable, also a basis for risk analysis of the machinery in order to reveal its vulnerabilities. This research investigates the following modelling approach in order to improve the efficiency of marine and offshore machinery operating under highly uncertain environment. Firstly, this study makes full use of evidential reasoning’s advantage to propose a novel fuzzy evidential reasoning sensitivity analysis method (FER-SAM) to facilitate the assessment of operational uncertainties (trend analysis, family analysis, environmental analysis, design analysis, and human reliability analysis) in ship cranes. Secondly, a fuzzy rule based sensitivity analysis methodology is proposed as a maintenance prediction model for oil-wetted gearbox and bearing with emphasis on ship cranes by formulating a fuzzy logic box (diagnostic table), which provides the ship crane operators with a means to predict possible impending failure without having to dismantle the crane. Thirdly, experience has shown that it is not financially possible to employ all the suggested maintenance strategies in the literature. Thus, this study proposed a fuzzy TOPSIS approach that can help the maintenance engineers to select appropriate strategies aimed at enhancing the performance of the marine and offshore machinery. Finally, the developed models are integrated in order to facilitate a generic planned maintenance framework for robust improvement and management, especially in situations where conventional planned maintenance techniques cannot be implemented with confidence due to data deficiency

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

A graph oriented approach for network forensic analysis

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex multi-stage intrusions. This dissertation presents a novel graph based network forensic analysis system. The evidence graph model provides an intuitive representation of collected evidence as well as the foundation for forensic analysis. Based on the evidence graph, we develop a set of analysis components in a hierarchical reasoning framework. Local reasoning utilizes fuzzy inference to infer the functional states of an host level entity from its local observations. Global reasoning performs graph structure analysis to identify the set of highly correlated hosts that belong to the coordinated attack scenario. In global reasoning, we apply spectral clustering and Pagerank methods for generic and targeted investigation respectively. An interactive hypothesis testing procedure is developed to identify hidden attackers from non-explicit-malicious evidence. Finally, we introduce the notion of target-oriented effective event sequence (TOEES) to semantically reconstruct stealthy attack scenarios with less dependency on ad-hoc expert knowledge. Well established computation methods used in our approach provide the scalability needed to perform post-incident analysis in large networks. We evaluate the techniques with a number of intrusion detection datasets and the experiment results show that our approach is effective in identifying complex multi-stage attacks

A two-level structure for advanced space power system automation

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed

Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection

This study combines Fuzzy Analytic Hierarchy Process (FAHP), Geographic Information System (GIS) and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP). The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future