A study on forecasting bigmart sales using optimized machine learning techniques

Data mining is an in-depth study of enormous amounts of data present in an organization or institution’s repository. Business experts mostly utilize data analytics approaches to confirm their opinion. It will rapidly boost the global interest of the organization. In this scenario, the information and conclusion are gathered from Data analysis by data analytics. The experts also use it to validate, diagnose, or authenticate speculate layouts suppositions and completion of the analysis. In this paper, the prediction is based on grocery data sets by inspecting and analyzing the big mart sales data set. Among several predictive algorithms, data mining algorithms are considered for prediction. It includes Decision Tree, Naïve Bayes, Adaboost with Particle Swarm optimization, and Random forest. The proposed method of this research is a novel Naïve Bayes with a PSO algorithm. This algorithm optimizes the model iteratively. Exploration of the data must be done before prediction. The root means squared error (RMSE) is used as evaluation metrics for comparing the data mining algorithms.  The proposed algorithm performs well and gives a lower RMSE value. So, the proposed algorithm fits the best model when compared with the existing algorithms. This paper describes the prediction of high-quality data analysis data and determines the efficiency of data mining algorithms

Influence of carbon nano tubes on mechanical, metallurgical and tribological behavior of magnesium nanocomposites

In this research work, three different reinforcements of Carbon Nano Tubes (in weight %) such as 2%, 3% and 4% were added to the magnesium AZ91D grade magnesium alloy to fabricate the Nanocomposites through stir casting method. The effects of volume percentage on the mechanical, metallurgical and wear behavior were analyzed. The composites with 4% reinforcement show high hardness while the composites with 3% reinforcement show better tensile and yield strength and also an improved wear resistance compared to other. Also, the characterization of the Nanocomposites were made using Optical Microscopy, Scanning Electron Microscopy, Finite Element – Scanning Electron Microscopy and Transmission Electron Microscopy to understand its nature. Keywords: CNT, Magnesium alloy, Stir casting, Wear behavio

Infertility and social issue have the most significant impact on health-related quality of life among polycystic ovarian syndrome women in South India

Background: Infertility can have a significant impact on the identity of women. Individual women, who are infertile, experience tragic emotions, as well as those who are sad for great losses, like the death of a loved one. In this case, the woman is experiencing the loss of the ability to procreate. Aim: In the present study, our major concern was to implement the health-related quality of life (HRQOL) Questionnaire on South Indian polycystic ovarian syndrome (PCOS) women to assess the impact of various clinical features of polycystic ovary syndrome on the HRQOL of South Indian women diagnosed. Settings and Design: A total of 126 females in the first phase and 356 females in the second phase between the age group of 18–40 years characterised under the Rotterdam criteria were selected for the study. Materials and Methods: The study was carried out in three different phases which included a one-to-one interview, group discussion and questionnaire session. In our study, we found that all the females who attend the study showed positivity for all the domains developed in the previous study and suggested that further domain can be developed. Statistical Analysis Used: Suitable statistical methods were used with Graph pad PRISM (version 6). Results: Hence, in our study, we developed a further new sixth domain called as 'social impact domain'. Among South Indian PCOS women, we found that infertility and social issue have the most significant impact on HRQOL. Conclusion: The revised questionnaire by including the sixth domain called 'Social issue' is likely to be useful in measuring the quality of health of female having PCOS in regard to South Indian population

Nanopower SAR ADCs with Reference Voltage Generation

This chapter targets low-power techniques for nanopower SAR ADCs with reference voltage generation. First of all, a 106nW 10b 80 kS/s SAR ADC with duty-cycled reference generation is presented, where a CMOS voltage reference, a duty-cycling block, and a LDO are integrated with the SAR ADC together. Furthermore, a low-power bidirectional comparator is utilized in the SAR ADC to reduce the power consumption. The reference-included SAR ADC achieves a FoM of 2.4fJ/conv.-step. Second, an energy-free DAC reset technique, “swap-to-reset,” is presented to deal with the large DAC reset energy in a SAR ADC, which is usually large compared with DAC conversion energy. In the prototype, the DAC energy consumption is reduced by one-third with “swap-to-reset” applied to the 2 MSBs. Finally, a low-power and area-efficient discrete-time reference driver is introduced. By calculating the energy consumption of each switching step, the DAC in a SAR ADC can be driven by a pre-charged decoupling capacitor compensated by a small auxiliary DAC. In the prototype, the SNDR/SFDR are improved by 2.7 dB/11.6 dB after enabling the 3b DAC compensation and the discrete-time reference driver only adds 10.8% and 10.1% to the power and chip area of the SAR ADC, respectively.</p