This study aims to analyze: 1) the effect of brand awareness on the purchase Intention of the customer CS Finance Tangerang area 2) the influence of brand awareness on perceived quality in customers CS Finance Tangerang area and 3) the influence of perceived quality on purchase decisition on CS Finance Tangerang area customers. This study uses a conclusive research design that is research designed to help decision making in determining, evaluating and choosing the best alternatives in solving research problems. The type of research that the author uses in the design of conclusive research is a type of descriptive research that is research that aims to explain the causal relationship between variables with the specification of a single cross-sectional design research technique in which data collection activities are carried out from one respondent for a particular moment. This research was analyzed with Structural Equation Modeling (SEM) analysis with Lisrel, a statistical modeling technique that is very cross-sectional, linear and general. Included in the Lisrel SEM is factor analysis, path analysis and regression. The study was conducted from February to August 2018. Respondents were 200 samples at CS Finance Tangerang area customers. The results of the study are: 1) the positive and significant influence of brand awareness on purchase intention on CS Finance Tangerang area customers 2) the positive and significant influence of brand awareness on perceived quality in consumers of CS Finance Tangerang area and 3) the influence positive and significant perceived quality of purchase intention in CS Finance Tangerang area consumers.
In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.
The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.
Effective information retrieval is defined as the number of relevant documents that are retrieved with respect to user query. In this paper, we present a novel data fusion in IR to enhance the performance of the retrieval system. The best data fusion technique that unite the retrieval results of nu merous systems using various data fusion algorith ms. The study show that our approach is more efficient than traditional approaches.
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