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This paper deals with a novel natural sampled pulse width modulation (PWM) switching strategy for voltage source inverter through carrier modification. The proposed inverted sine carrier PWM (ISCPWM) method, which uses the conventional sinusoidal reference signal and an inverted sine carrier, has a better spectral quality and a higher fundamental component compared to the conventional sinusoidal PWM (SPWM) without any pulse dropping. The ISCPWM strategy enhances the fundamental output voltage particularly at lower modulation index ranges while keeping the total harmonic distortion (THD) lower without involving changes in device switching losses. The presented mathematical preliminaries for both SPWM and ISCPWM give a conceptual understanding and a comparison of the strategies. The detailed comparison of the harmonic content and fundamental component of the ISCPWM output for different values of modulation index with the results obtained for the SPWM is also presented. Finally, the proposed modulator has been implemented in field programmable gate array (FPGA- Xilinx Spartan 3) and tested with the proto-type inverter

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This paper illustrates the process of applying data mining for finding success rate of In-Vitro Fertilization treatment. The data set used in the experiments contains information recorded during the IVF treatment. In the research paper defined the supportive information to the medical practioner for knowing the success rate of patient before starting the Artificial Insemination (11). In the IVF, the doctors and patients may don't know the way of predicting the success rate of the treatment (9). The success rate may help the patients to be getting ready for the treatment physically and psychologically (8). In data mining has many tools for data reduction and prediction (5). Rough Set Theory (RST) used for the data cleaning and reduction (13). It presents the influential parameters of the IVF treatment as an output. The Artificial Neural Network (ANN) gets the output of the RST as an input and built a network for the input and produce desired output. So the processes checked the result of patient and compare the desired and actual output (6). This experiment is a way of study which is related to the representativeness of the sample and irrelevant features. Out of around 250 million individuals estimated to be attempting parenthood at any given time, 13 to 19 million couples are likely to be infertile. So the couples prefer the IVF treatment compared with other methods of treatment (5). In India the board of medical council announced the duration of infertility. If a woman was not conceived after his marriage within 6 months they caused infertility [14]. So they must start the initial fertility treatment. Most of them prefer the In-Vitro fertilization compare with other fertility treatments (3). A survey of fertility treatment showed 1 in 20 of all pregnancies conceived by the IVF treatment. But the patients suffer from the negative imagination and they don't know the success level of the treatment (1). It is very essential to analyze the data set and reduce or clean the unwanted data that increases the prediction accuracy in a proper manner (15). The parameters with high impact factor can be selected by applying the proper reduct algorithm which removes the parameters that has a lesser role in determining the success rate of particular patients and help the Gynecologists to recommend them for specific treatment of IVF, IUI or ICSI (1).

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The education sector has been effectively dealing with the prediction of academic performance of the Immigrant students since the research associated with this domain proves beneficial enough for those countries where the ministry of education has to cater to such immigrants for altering and updating policies in order to elevate the overall education pedagogy for them. The present research begins with analyzing varied educational data mining and machine learning techniques that helps in assessing the data fetched form PISA. It’s elucidated that XGBoost stands out to be the ideal most machine learning technique for achieving the desired results. Subsequently, the parameters have been optimized using the hyper parameter tuning techniques and implemented on the XGBoost Regressor algorithm. Resultant there is low error rate and higher level of predictive ability using the machine learning algorithms which assures better predictions using the PISA data. The final results have been discussed along with the upcoming future research work.

Clustering is a method in data mining which deals with huge amount of data. Clustering is intended to assist a consumer in discovering and know-how the herbal structure in a statistics set and abstract the which means of massive dataset. It is the undertaking of partitioning objects of a statistics set into awesome businesses such that two gadgets from one cluster are similar to every other, while objects from wonderful clusters are assorted. Clustering is unsupervised getting to know in which we are not provided with instructions, in which we will area the records items.With the arrival growth of high dimensional statistics including microarray gene expression facts, and grouping excessive dimensional statistics into clusters will come across the similarity among the items in the full dimensional area is frequently invalid as it consists of exclusive styles of information. The technique of grouping into high dimensional information into clusters is not accurate and possibly not as much as the extent of expectation when the dimension of the dataset is high.

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