scholarly journals Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data

2021 ◽  
Vol 8 (4) ◽  
pp. 547-556

Regressions have been continuously received great attention. However, there are still open issues in regression, and two of the issues is regression with multicollinearity and outlier. Regularization (Ridge, Lasso, and Elastic Net) techniques implement a means to control regression coefficients. The methods can decrease the variance and reduce our sample error for tackle multicollinearity. In robust regression, it is a form of regression method designed to overcome outliers. Robust regression is an important method for analyzing data that are infected with outliers. The data have been interacted on the second order interaction. The data contained 435 different independent interaction variables. The primary focus of this paper is to analyze and compare the impact of three different variable selection techniques regularization regression algorithms for the data seaweed drying. After that, it will be analyzed through robust regression (Tukey Bi-Square, Hampel, and Huber). As the result, the Lasso-Hampel was better than others with the MAE (4.09641), RMSE (5.275992), MAPE (7.9962), SSE (182491.2), R-square (0.6514791), and R-square Adjusted (0.649279). The method of Lasso-Hampel is able to be relied on investigation of the accuracy in big data obtained from regularization and robust regression.

2019 ◽  
Vol 8 ◽  
pp. 54-56
Author(s):  
Ashmita Dahal Chhetri

Advertisements have been used for many years to influence the buying behaviors of the consumers. Advertisements are helpful in creating the awareness and perception among the customers of a product. This particular research was conducted on the 100 young male and female who use different brands of product to check the influence of advertisement on their buying behavior while creating the awareness and building the perceptions. Correlation, regression and other statistical tools were used to identify the relationship between these variables. The results revealed that the relationship between media and consumer behavior is positive. The adve1tising impact on sales and there is positive and high degree relationship between advertising and consumer behavior. The impact on advertising of a product of electronic media is better than non-electronic media.


2019 ◽  
Vol 10 (4) ◽  
pp. 106
Author(s):  
Bader A. Alyoubi

Big Data is gaining rapid popularity in e-commerce sector across the globe. There is a general consensus among experts that Saudi organisations are late in adopting new technologies. It is generally believed that the lack of research in latest technologies that are specific to Saudi Arabia that is culturally, socially, and economically different from the West, is one of the key factors for the delay in technology adoption in Saudi Arabia. Hence, to fill this gap to a certain extent and create awareness about Big Data technology, the primary goal of this research was to identify the impact of Big Data on e-commerce organisations in Saudi Arabia. Internet has changed the business environment of Saudi Arabia too. E-commerce is set for achieving new heights due to latest technological advancements. A qualitative research approach was used by conducting interviews with highly experienced professional to gather primary data. Using multiple sources of evidence, this research found out that traditional databases are not capable of handling massive data. Big Data is a promising technology that can be adopted by e-commerce companies in Saudi Arabia. Big Data’s predictive analytics will certainly help e-commerce companies to gain better insight of the consumer behaviour and thus offer customised products and services. The key finding of this research is that Big Data has a significant impact in e-commerce organisations in Saudi Arabia on various verticals like customer retention, inventory management, product customisation, and fraud detection.


Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 758
Author(s):  
Fiona Esam ◽  
Rachel Forrest ◽  
Natalie Waran

The influence of the COVID-19 pandemic on human-pet interactions within New Zealand, particularly during lockdown, was investigated via two national surveys. In Survey 1, pet owners (n = 686) responded during the final week of the five-week Alert Level 4 lockdown (highest level of restrictions—April 2020), and survey 2 involved 498 respondents during July 2020 whilst at Alert Level 1 (lowest level of restrictions). During the lockdown, 54.7% of owners felt that their pets’ wellbeing was better than usual, while only 7.4% felt that it was worse. Most respondents (84.0%) could list at least one benefit of lockdown for their pets, and they noted pets were engaged with more play (61.7%) and exercise (49.7%) than pre-lockdown. Many respondents (40.3%) expressed that they were concerned about their pet’s wellbeing after lockdown, with pets missing company/attention and separation anxiety being major themes. In Survey 2, 27.9% of respondents reported that they continued to engage in increased rates of play with their pets after lockdown, however, the higher levels of pet exercise were not maintained. Just over one-third (35.9%) of owners took steps to prepare their pets to transition out of lockdown. The results indicate that pets may have enjoyed improved welfare during lockdown due to the possibility of increased human-pet interaction. The steps taken by owners to prepare animals for a return to normal life may enhance pet wellbeing long-term if maintained.


2021 ◽  
pp. 115076
Author(s):  
Covadonga Díez-Sanmartín ◽  
Antonio Sarasa-Cabezuelo ◽  
Amado Andrés Belmonte

Work ◽  
2020 ◽  
Vol 67 (3) ◽  
pp. 557-572
Author(s):  
Said Tkatek ◽  
Amine Belmzoukia ◽  
Said Nafai ◽  
Jaafar Abouchabaka ◽  
Youssef Ibnou-ratib

BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis. OBJECTIVES: The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread’s rate of COVID-19. It will also facilitate countries’ interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic. METHODS: The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries’ abilities and tools in monitoring, combating, and predicting the spread of COVID-19. RESULTS: The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19. CONCLUSIONS: This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.


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