Earticle

The relationship between med-long term load forecasting and socio-economic indicators is very difficult to describe with an accurate mathematical model. The paper introduce data mining technology into the association analysis of China's electricity consumption growth, select many socio-economic indicators since 2000, constitute the relevant factors database, complement of a few missing data, and dig out a number of indicators closely related to the electricity consumption with cluster analysis, and the data of distortion indicators is corrected, thus, build a more scientific load forecasting model. Validate and test the correlation of electricity consumption and selected indicators by dynamic neural network time sequence tool. The results show that the prediction model has good convergence, and the effect is satisfactory.

This research work proposes a Weighted Least Square Twin Support Vector Machine (WLSTSVM) for imbalanced dataset. Real world data are imbalanced in nature due to which most of the classification techniques do not work well. In Imbalanced data, there is a huge difference between the numbers of data samples of classes. One class data samples are larger as compared to other class data samples. This paper discusses the traditional methods of handling imbalanced data and proposes an improvement over Least Square Twin Support Vector Machine. This research work has performed experiment on five benchmark UCI datasets using 10-fold cross validation method. The results of experiment show that the proposed technique performed well for imbalanced dataset and its accuracy is better as compared to other existing methods. This research work presents the formulation of proposed approach for both linear and non-linear data samples.

In the last few years, RFID is commonly used in many related fields of new service application and new study as one of the significant technological advancements, such as science manufacturing, logistics transportation, traffic management, medical information, and so on. Those intelligent and automatic innovative products gradually take the place of manpower. Due to low cost and remote automatic identification advantages, RFID technology seems to be a popular current trend. In RFID database, there is a vast number of RFID trajectory data with the spatial-temporal characteristic. How to extract the traveling partners from these data is a difficult problem. For solving the difficult problem, we proposed an algorithm called MTP to discovery the traveling partners from RFID database, it uses a intersecting method to mine moving objects with moving together in a period of time. Meanwhile, we analyze the performance of MTP, the result of our experiment demonstrate that the MTP algorithm is suited to mine the traveling partners.

Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network services. Several machine-learning paradigms including neural networks, linear genetic programming (LGP), support vector machines (SVM), Bayesian networks, multivariate adaptive regression splines (MARS) fuzzy inference systems (FISs), etc. have been investigated for the design of IDS. In this paper, we develop a hybrid method of C5.0 and SVM and investigate and evaluate the performance of our proposed method with DARPA dataset. The motivation for using the hybrid approach is to improve the accuracy of the intrusion detection system when compared to using individual SVM and individual SVM.

This paper mainly constructs a unified model for formal contexts, consistent formal decision contexts and inconsistent formal decision contexts based on object oriented concept lattices, which is called a consistent approximate representation space. Congruence relations are first introduced into formal contexts and then relationships between congruence relations and the corresponding object oriented concept lattices are developed. Finally, consistent approximate representation space is defined for a formal context, which is a quadruple (U ,A ,R ,R'). It is verified that if we give R different meanings, then we obtain the corresponding formal context, consistent formal decision context and inconsistent formal decision context with respect to object oriented concept lattices. Therefore, the quadruple ( ,A ,R ,R') is a unified model for formal contexts and formal decision contexts.

Aimed at the real-time request of dynamic scheduling to product resource, an optimization method of parallel dynamic chain real-time available resources was put forward. Proceed from real-time tracing of resource information influencing scheduling task’s dynamic property, established a resource information’s real-time tracing back and optimization model, which used module and parallel process mechanism to different kinds of real-time traced back resource information. The mechanism processed global dynamic feedback tracing for every module and optimized available resources primarily, on this basis, graded the primary available resources and gave a real-time candidate resource set. Through one example of one gear production scheduling, the method’s validity was tested.

With the large-scale application of high dimensional gene expression data which exists lots of redundant information, it may waste a lot of time in feature selection and classification. By analyzing the process of MapReduce computing paradigms on cloud platform, it is found that the feature selection which through parallel and distributed computing in MapReduce combined with extreme learning machine is appropriate for constructing a recognition method. This paper proposed a MapReduce algorithm on high gene feature for parallel and distributed selection and classification, aiming to save time resources to make a higher accuracy in training process on large scale gene datasets. Simulation experiments on gene datasets show that the running time on cloud platform is greatly shortened by the time promising the high classification accuracy.

Based on the bibliography references included in ISI Web of Knowledge, and by use of visualization network analysis tool——Citespace to conduct co-citation and cluster analysis of collected data, this paper renders knowledge mapping on the development and evolution of information resource allocation research, reveals two different periods of information resource allocation research, and identifies representative scientists as well as relevant literatures in each period. Meanwhile, this paper demonstrates the hot research fields represented by enterprise resource allocation, theory and practice research of information resource allocation, biological information resource allocation, retrieval and sharing research, and computer network communication resource allocation, and further presents the frontiers represented by empirical research of information resource allocation, information retrieval about resource allocation, network communication resource allocation, biological information data mining, biology knowledge management, gene-ontology and semantic web.

Ontology evolution in collateral environments mainly features that multiple users modify the same ontology. All of the ontology change sequences submitted by the users may not be done to the ontology because there are conflicts between them. Unlike previous approach that some ontology change sequences must be removed, this paper focuses on the relationship between ontology changes rather than ontology change sequences. We defined a dependence relationship between two ontology changes and searched all the dependences from different ontology change sequences. We constructed a direct graph for ontology changes with the dependences set. On this basis, we proposed an algorithm based on Traveling Salesmen Problem to find a suitable evolution path. A prototype is implemented and the experiment showed that our approach could keep more ontology changing.

To improve the accuracy of memory based recommendation while keeping the low time cost, an expected item bias (EIA) based similarity computation is proposed. And a hybrid approach (HA) integrating the global rating information and local rating information is also proposed. The features of two classical datasets MovieLens and Netflix for recommendation system benchmarking are anglicized. The experiments on MovieLens and Netflix show that both EIA and HA could improve the performance alone. A combinational use of them will lead even better results on the two benchmark datasets.

The illiteracy rate in Pakistan is sky-scraping as compare to the other developed countries of the world. Being illiterate is not only a single person disability; it also has some social implications. It is very hard for the democratic institutions and values to prosper in a society where half of the adult population is uneducated, and most of the citizen cannot access information or read newspapers. The situation seems to be more critical when a large number of students drop their education in a mid-way because of some factors. Pakistan is one of the countries of the world in which the illiterate peoples are great in numbers. This study was conducted to examine these factors as well as the basic reasons which have some influence on the academic performance of the students belongs to higher secondary education especially in big cities of Pakistan like in Karachi. The respondents of this study were the students who are either currently involved in the pre-graduate studies i.e. Higher Secondary studies of part I and part II or have gone through this educational level most recently. A survey was conducted by using a questionnaire for information gathering about different factors relating to academic performance of students and dropouts. Percentage and different Hypothesis techniques were applied to analyze the data. The results of the study revealed that the “Poverty” and “Unawareness about the importance of education” collectively play a major role regarding the dropout rate of students during their pursuit for higher secondary education. Under the viewpoint of performance of the students, it was anticipated that “Cheating” and “Politics” jointly play a part for students’ progress towards higher secondary education in Karachi.