Showing 8 results for Networks
A. Aghaie,
Volume 20, Issue 1 (5-2009)
Abstract
Modern business organizations have appreciated the significance of having competitive advantage through the delivery of continuous improvement towards the customers, and being knowledge-oriented. Indisputably, Knowledge Management (KM) plays a key role in the success of Customer Relationship Management (CRM). In this regard, Customer Knowledge Management (CKM) is a newly developed concept that deals with knowledge from customers rather than knowledge about customers. However, little research has been done on the application of CKM in e-business. In this paper, after an overview of the literature, an application of CKM in Customer Lifetime Value (CLV) measurement is studied in an e-retailer case where Corporate Image and Reputation are taken into consideration.
Sujit Kumar Jha,
Volume 23, Issue 3 (9-2012)
Abstract
This paper presents an overview of new approaches in rapid product development in production networks from design points of view. The manufacturing industries are changing their focus to global sourcing as a means to improve performance and enhance competitiveness. Some partnerships created with this strategy improve product development through collaborative design. With the advent of e-Commerce, a new set of collaborative applications integrated to the firms’ IT infrastructure allow a direct interaction between the firm and its suppliers, having an impact of negotiations. The globalization of the market necessitates the reduction of time-to-market, mainly due to shorter product life cycle. The computing and communication have become indispensable in every aspect of product development and design. The paper describes the network that directly links designer capabilities and with customers and manufacturing division. The networks focuses the three major forces that will affect the design community, namely, speed of decision, expansion of scope and degree of concurrency. Due to evolution of production networks, it has become possible to obtain the mass production within a key short time, using emerging technology that affect the speed and efficiency of product development using a pool of efficient designers and product managers.
Mehdi Khashei , Farimah Mokhatab Rafiei, Mehdi Bijari ,
Volume 23, Issue 4 (11-2012)
Abstract
In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more satisfactory than other those models. Therefore, it can be a suitable alternative model for interval forecasting of financial time series.
Mojtaba Torkinejad, Iraj Mahdavi, Nezam Mahdavi-Amiri, Mirmehdi Seyed Esfahani,
Volume 28, Issue 4 (11-2017)
Abstract
Considering the high costs of the implementation and maintenance of gas distribution networks in urban areas, optimal design of such networks is vital. Today, urban gas networks are implemented within a tree structure. These networks receive gas from City Gate Stations (CGS) and deliver it to the consumers. This study presents a comprehensive model based on Mixed Integer Nonlinear Programming (MINLP) for the design of urban gas networks taking into account topological limitations, gas pressure and velocity limitations and environmental limitations. An Ant Colony Optimization (ACO) algorithm is presented for solving the problem and the results obtained by an implementation of ACO algorithm are compared with the ones obtained through an iterative method to demonstrate the efficiency of ACO algorithm. A case study of a real situation (gas distribution in Kelardasht, Iran) affirms the efficacy of the proposed approach.
N. Desai, S. Venkatramana, B.v.d.s. Sekhar,
Volume 31, Issue 3 (9-2020)
Abstract
Today's digital world demands about automated sentiment analysis on visual and text content to significantly displaying people's feelings, opinions and emotions through text, images and videos across popular social networks. Earlier visual sentimental analysis faces many drawbacks like achieve low accuracy and more difficult to understand people opinions due to traditional techniques. Also, another major challenge is a huge number of images generated and uploaded every day across the world. This paper overcomes problems of visual sentiment analysis with the help of deep learning convolution neural network (CNN) and Affective Regions approach to achieve more meaningful sentiment reports with huge accuracy.
Fatemeh Elhambakhsh, Mohammad Saidi- Mehrabad,
Volume 32, Issue 1 (1-2021)
Abstract
Statistical monitoring of dynamic networks is a major topic of interest in complex social systems. Many researches have been conducted on modeling and monitoring dynamic social networks. This article proposes a new methodology for modeling and monitoring dynamic social networks for quick detection of temporal anomalies in network structures using latent variables. The key idea behind our proposed methodology is to determine the importance of latent variables in creating edges between nodes as well as observed covariates. First, latent space model (LSM) is used to model dynamic networks. Vector of parameters in LSM model are monitored through multivariate control charts in order to detect changes in different network sizes. Experiments on simulated social network monitoring demonstrate that our surveillance monitoring strategy can effectively detect abrupt changes between actors in dynamic networks using latent variables.
Fatemeh Elhambakhsh, Kamyar Sabri-Laghaie,
Volume 33, Issue 1 (3-2022)
Abstract
The fourth industrial revolution has changed our lives by enabling everyone to be interconnected virtually. A trustworthy system is required to secure large volume of stored data in IoT-based devices. Blockchain technology has led to transfer and to save data in a safe way. With this in mind, the blockchain-based cryptocurrencies have gained quite a bit of popularity because of their potential for financial transactions. In this regard, monitoring transactions network is very fruitful to find users’ abnormal behaviors. In this research, a novel procedure is used to monitor blockchain cryptocurrency transactions network. To do so, a random, binary graph model is used to simulate the transactions between users, and a SCAN method is used to detect the abnormal behaviors in the simulated model. Also, a multivariate exponentially weighted moving average (MEWMA) control chart is used to monitor centrality measures. The probability of signal is used to assess the performance of the SCAN method and that of the MEWMA control chart in distinguishing abnormalities. Then, the procedure is adopted to a Bitcoin transactions dataset.
Hojjat Pourfereidouni, Hasan Hosseini-Nasab,
Volume 34, Issue 2 (6-2023)
Abstract
This paper proposes a data-driven method, using Artificial Neural Networks, to price financial options and compute volatilities, which speeds up the corresponding numerical methods. Prospects of the Stock Market are priced by the Black Scholes model, with the difference that the volatility is considered stochastic. So, we propose an innovative hybrid method to forecast the volatility and returns in Stock Market indices, which declare a model with a generalized autoregressive conditional heteroscedasticity framework. In addition, this research analyzes the impact of COVID-19 on the option, return, and volatility of the stock market indices. It also incorporates the long short-term memory network with a traditional artificial neural network and COVID-19 to generate better volatility and option pricing forecasts. We appraise the models' performance using the root second-order quadratic function means of the out-of-sample returns powers. The results illustrate that the autoregressive conditional heteroscedasticity forecasts can serve as informative features to significantly increase the predictive power of the neural network model. Integrating the long short-term memory and COVID-19 is an effective approach to construct proper neural network structures to boost prediction performance. Finally, we interpret the sensitivity of option prices concerning the market or model parameters, which are essential in practice.
