Analyzing and processing massive volumes of data in different applications like sensor data, health care and e-Commerce require big data processing technologies. Extracting useful information from the enormous size of unstructured data is a crucial thing. As the amount of data becomes more extensive, sophisticated pre-processing techniques are required to analyze the data. In social networking sites and other online shopping sites, a massive volume of online product reviews from a large size of customers are available [1]. The impact of online product reviews affects 90% of the current e-Commerce market [2]. Customer reviews contribute the product sale to an extent and product life in the market depends on online product recommendations. Online feedback is one of the communication methods which gives direct suggestions from the customers [3, 4]. Online reviews and ratings from customers are another information source about product quality [5, 6]. Customer reviews can help to decide on a new successful product launch. Online shopping has several advantages over retail shopping. In retail shopping, the customers visit the shop and receive price information but less product
<span>The hardware implementation of sensorless brushless direct current motor drive incorporating H-infinity control strategy with optimized weights by particle swarm optimization in the speed control is carried out in this work. The methodology involved in the design of brushless direct current (BLDC) motor control with sensorless position detection technique, the design of H-infinity speed controller, steps involved in particle swarm optimization for optimizing coefficients of its weights and the hardware implementation is discussed in detail in this paper. Texas Instruments microcontroller board C2000 Delfino Launchpad LAUNCHXL F28377S and driver BOOSTXL DRV8301 are used for realization of the speed controller. The code is developed using C2000 hardware support package in MATLAB/SIMULINK platform. A comprehensive performance analysis is accomplished during starting of the motor and during the fast application and removal of load. This strategy is found to be robust resulting in faster load disturbance rejection and better reference speed tracking. The experimental results of the proposed strategy are compared with that of conventional proportional-integral (PI) controller. The time domain parameters are also compared. It is found that the proposed strategy exhibits better performance characteristics during transients and sudden disturbances in load.</span>
Purpose
Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue.
Design/methodology/approach
The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider.
Findings
The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents.
Research limitations/implications
The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour.
Practical implications
This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit.
Originality/value
This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.
Pre-launch success prediction of a product is a challenge in today's electronic world. Based on this prediction, industries can avoid huge losses by deciding on whether to launch or not to launch a product into the market. We have implemented a Multithreaded Hash join Resilient Distributed Dataset (MHRDD) with a prediction classifier for pre-launch prediction. MHRDD helps to remove the redundancy in the input dataset and improves the performance of the prediction model. Large volume of e-Word of Mouth (e-WOM) data like product reviews, comments and ratings available on internet about products can be used for pre-launch product prediction. In MHRDD, to identify features a distance similarity score is used. In order to remove duplicates, a hash key and join operations are used to create a hash table of significant features. With in-memory computations and hashing on the join operations, this model reduces redundancy of data. This model is scalable and can handle large datasets with good prediction accuracy. This paper presents a novel big data processing method that predicts product success before its launch in the market. Proposed method helps to identify features that are significant for the product to be successful. Based on the pre-launch prediction, companies can reduce cost, effort and time with improved product success.
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