Tripod of Requirements in Horizontal Heterogeneous Mobile Cloud Computing

Recent trend of mobile computing is emerging toward executing resource-intensive applications in mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of cloud computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment computing and storage capabilities of mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to cloud computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on mobile-cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency

An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

Realistic and Efficient Radio Propagation Model for V2X Communications

Multiple wireless devices are being widely deployed in Intelligent Transportation System (ITS) services on the road to establish end-to-end connection between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. Vehicular ad hoc networks (VANETs) play an important role in supporting V2V and V2I communications (also called V2X communications) in a variety of urban environments with distinct topological characteristics. In fact, obstacles such as big buildings, moving vehicles, trees, advertisement boards, traffic lights, etc. may block the radio signals in V2X communications. Their impact has been neglected in VANET research. In this paper, we present a realistic and efficient radio propagation model to handle different sizes of static and moving obstacles for V2X communications. In the proposed model, buildings and large moving vehicles are modeled as static and moving obstacles, and taken into account their impact on the packet reception rate, Line-of-sight (LOS) obstruction, and received signal power. We use unsymmetrical city map which has many dead-end roads and open faces. Each dead-end road and open faces are joined to the nearest edge making a polygon to model realistic obstacles. The simulation results of proposed model demonstrates better performance compared to some existing models, that shows proposed model can reflect more realistic simulation environments.Khokhar, RH.; Zia, T.; Ghafoor, KZ.; Lloret, J.; Shiraz, M. (2013). Realistic and Efficient Radio Propagation Model for V2X Communications. KSII Transactions on Internet and Information Systems. 7(8):1933-1954. doi:10.3837/tiis.2013.08.011S193319547

Classification with degree of importance of attributes for stock market data mining

With the increase of economic globalization and evolution of information technology, financial time series data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment for decisionmaking. Many statistical and data mining techniques have been used to predict time series stock market. However, most statistical and data mining methods suffer from serious drawback due to requiring long training times, results are often hard to understand, and producing inaccurate predictions. We present another modification of fuzzy decision tree (FDT) classification techniques that aims to combine symbolic decision trees in data classification with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: ability to learn from examples, high knowledge comprehensibility of decision trees, and the ability to deal with uncertain information of fuzzy representation. In particular, the proposed predictive fuzzy decision tree is based on the concept of degree of importance of attribute contributing to the classification. We extend this idea with the expressive power of fuzzy reasoning method. After constructing predictive FDT, weighted fuzzy production rules (WFPRs) can be extracted from predictive FDT. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that predictive FDT algorithm can generate a relatively optimal tree without much computation effort (comprehensibility), and WFPRs have a better predictive accuracy of stock market time series data. Many attempts have been made for meaningful prediction from real time stock market data by using data mining and statistical techniques such as Support Vector Machine [1,2], and Linear and Non- Linear Statistical Models [3,4], Neural Networks [5, 6]. Alan Fan et aI., [2] use Support Vector Machine (SVM) to stock market prediction. The SVM is a training algorithm for learning classification and regression rules from data [7]. However the predictive accuracy of SVM achieved by [2] in stock market is relatively lower than other classification applications [8, 9]. Also the existing relationship between the future stock returns and its accounting information, one would expect it to be a weak relationship. Support Vector Regression (SVR) is the extended form of SVM that can be applied in financial time series prediction [8, 9]. In financial data, due to the embedded noise, one must set a suitable margin in order to obtain a good prediction [9]. Haiqin et at, [9] has extended the standar

Development of a compact linguistic rules-tree (CLR-Tree) : the first phase.

Classification in data mining is very extensive research area. Decision trees have been found very effective for classification of huge and frequently modifiable databases e.g., Stock Exchange, Shopping Mall etc. We build a decision tree from a training set consists of two phases. In the first phase the initial Linguistic Rules-Tree (LR¬Tree) has been constructed. In LR-Tree we have combined fuzzy logics and decision tree. First we evaluate fuzzy membership function from training data for each attribute in class then apply our fuzzy linguistic approach which is associated with decision tree that provides for a fine grain description of classified items adequate for human reasoning. Consequently our approach will be able to handle training data with missing attribute values, handling attributes with differing costs, improving computational efficiency. But LR-Tree may not be the best generalization due to over-fitting so in the second phase, we will propose a novel frequent pattern mining tree called Compact Linguistic Rules-Tree (CLR-Tree) that remove some branches and nodes to improve the accuracy of the classifier. In this paper, we have concentrated on construction phase and hope that after completing the construction phase we will proof that the CLR- Treeis efficient and scalable for mining both long and short frequent patterns

Predictive fuzzy reasoning method for time series stock market data mining

Data mining is able to uncover hidden patterns and predict future trends and behaviors in financial markets. In this research we approach quantitative time series stock selection as a data mining problem. We present another modification of extraction of weighted fuzzy production rules (WFPRs) from fuzzy decision tree by using proposed similarity-based fuzzy reasoning method called predictive reasoning (PR) method. In proposed predictive reasoning method weight parameter can be assigned to each proposition in the antecedent of a fuzzy production rule (FPR) and certainty factor (CF) to each rule. Certainty factors are calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, and effect of political situation from stock market. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that WFPRs rules have high learning accuracy and also better predictive accuracy of stock market time series data

Reducing handover latency in mobile IPv6-based WLAN by parallel signal execution at layer 2 and layer 3

The emergence of wireless networking demands continuous connectivity to support end-to-end TCP or UDP sessions. Wireless networking does not provide reliable connections to mobile users for real-time traffic such as voice-over IP, audio streaming and video streaming. Handover latency in Mobile IPv6 is one factor that disconnects users while roaming. Most of the proposed methods to reduce handover latency include either layer 2 or layer 3 design considerations. This paper discusses the mobile IPv6 handover process and proposes an efficient handover scheme for reducing the overall handover latency in Mobile IPv6. The proposed scheme consists of parallel signal execution at the access router while the mobile node is performing layer 2 handover. Simulation results shows that the proposed scheme performs better than the standard MIPv6 delays