Abstract In the view of the current fire safety emergency management system construction exists many problems, we should change the treatment, prevention and early warning of ideas, innovative thinking, and expounds application of the emergency management to build and improve the fire safety emergency management system. Inspired by this principle, this paper proposes the novel computer aided fire safety engineering model based on the shortest path algorithm. In our... In the view of the current fire safety emergency management system construction exists many problems, we should change the treatment, prevention and early warning of ideas, innovative thinking, and expounds application of the emergency management to build and improve the fire safety emergency management system. Inspired by this principle, this paper proposes the novel computer aided fire safety engineering model based on the shortest path algorithm. In our methodology, we primary focus on the following aspects. (1) To build a fast and efficient aftermath disposal system composed of information management subsystem, the science and technology support subsystem, emergency mobilization guarantee subsystem and the rehabilitation subsystem and other functional subsystems. (2) Construction of the science highly effective emergency early warning system must establish the perfect emergency circular and the warning system that establishes the perfect emergency medical system. (3) The construction of the efficient emergency response system. We have set up the sound scientific organization system for fire control safety emergency management decision making, based on the functions of three level theories. In the future, we will apply the model into real scenarios. Read More Read Less

Abstract Queries submitted by users to search engines might be ambiguous, concise and their meaning may change over time. Web query classification is emphasized by various search engines nowadays due to the increase in the size of the web, as millions of web pages are added to it every day. Some of the current Information Retrieval (IR) systems like Library Online Public Access Catalog (OPAC), dialog system and numerous... Queries submitted by users to search engines might be ambiguous, concise and their meaning may change over time. Web query classification is emphasized by various search engines nowadays due to the increase in the size of the web, as millions of web pages are added to it every day. Some of the current Information Retrieval (IR) systems like Library Online Public Access Catalog (OPAC), dialog system and numerous web search engines need classical Boolean approaches in addition to the current supervised methods. Document retrieval is a process which mainly involves retrieval of relevant documents for user queries and matching the results using efficient algorithms like 'page rank' and 'learn to rank' algorithms. In this paper, to retrieve more relevant documents against user query by reducing non-relevant documents, we proposed a tree based classification of web queries using Simple Ordinal Classification (SOC) and navigation of search keywords is performed dynamically with search session time of users. This method reduces the retrieval of most of the non-relevant documents and navigation cost using efficient B-tree data structure. It provides documents that match all keywords present in the user query and best resulting web pages for users with different categories of interest. We built a prototype application to evaluate the proposed approach. Our experimental results revealed that SOC has significant performance improvement with existing approaches. Read More Read Less

Abstract Convenience store location is always the primary factor, which is also an important factor. Suitable location can not only determine the number of store customers, but also determine the level of sales, which is the key to the success or failure of store operations. In order to set up the location of the commercial convenience store, we first discuss the... Convenience store location is always the primary factor, which is also an important factor. Suitable location can not only determine the number of store customers, but also determine the level of sales, which is the key to the success or failure of store operations. In order to set up the location of the commercial convenience store, we first discuss the importance of the convenience store location, and then analyze the influence factors of the convenience store location. There are many factors that determine the success of a convenience store, and the quality of the store address plays a key role in the success of the convenience store. Geospatial metadata has the characteristics of abstract, complex and multi dimension, while the traditional metadata expression is abstract, the form of expression is single, the expression dimension is limited, and the cognitive efficiency is not high. In order to improve the effectiveness and efficiency of geospatial metadata services, we introduce information visualization technology and information retrieval technology based on the analysis of the technology of information visualization and geographic spatial metadata on the characteristics, influence of geospatial metadata visualization factors, single factor and multi factor metadata visualization technology with the core application process of metadata visualization. Experimental results prove that the proposed model can obtain the better performance compared with the other models. Read More Read Less

Abstract Recently extreme learning machine (ELM) was proposed as a new learning method for single hiddenlayer feedforward neural networks (SLFNs), it is not the same as traditional gradient based learning algorithm strategies as it can achieve good generalization performance as well as extremely fast learning speed. However, ELM may require large number of hidden neurons due to the random determination of the input weights and hidden biases, and there... Recently extreme learning machine (ELM) was proposed as a new learning method for single hiddenlayer feedforward neural networks (SLFNs), it is not the same as traditional gradient based learning algorithm strategies as it can achieve good generalization performance as well as extremely fast learning speed. However, ELM may require large number of hidden neurons due to the random determination of the input weights and hidden biases, and there may exist a set of non-optimal parameters which lead ELM not be able to reach the global optimum in some cases. With the help of ideas that using a hybrid approach which takes advantage of the optimization method and ELM to train SLFNs, this study proposes a novel hybrid approach based on artificial bee colony (ABC) optimization method to optimize the ELM parameters, where the optimal input weights and biases of ELM are specified by the ABC approach and Moore- Penrose (MP) generalized inverse to analytically determine the output weights. The proposed algorithm, named ABCELM, is rigorously compared with the original ELM and other evolutionary ELM methods in different classification datasets. The obtained results clearly confirm that the proposed approach is more suitable for classification problems that we investigated, and it can not only achieve better generalization performance but be more robust with much more compact networks. Read More Read Less

Abstract The integration of Geographic Information Systems (GIS) and multi-criteria decision analysis (MCDA) is a privileged and a crucial way to evolve GIS into a real decision support system in the industrial site selection field. This study combines PROMETHEE II (Preference Ranking Organization METHod for Enrichment Evaluations) and Geographic Information System (GIS) to provide decision makers with a ranking model for industrial... The integration of Geographic Information Systems (GIS) and multi-criteria decision analysis (MCDA) is a privileged and a crucial way to evolve GIS into a real decision support system in the industrial site selection field. This study combines PROMETHEE II (Preference Ranking Organization METHod for Enrichment Evaluations) and Geographic Information System (GIS) to provide decision makers with a ranking model for industrial sites in Algeria. The ranking is based on a detailed study of geographic, environmental and socioeconomic criteria. A mixed integration mode between GIS and MCDA is proposed in this study. The result obtained by R-PRO (Ranking PROMETHEE) for ranking industrial zones in Algeria is refined by viewing the GISIZ (GIS for Industrial Zones). The system was designed for the evaluation of a new methodology of multi-criteria analysis guided by data mining. Only the Decision Making Support System (DMSS) part is presented in this paper. Read More Read Less