Cancer-related medical expenses and labour loss cost annually $10,000 million worldwide. Lung cancerrelated deaths exceed 70,000 cases globally every year. Furthermore, 225,000 new cases were detected in the United States in 2016, and 4.3 million new cases in China in 2015. Statistically, most lung cancer-related deaths were due to late-stage detection. Like other types of cancer, early detection of lung cancer could be the best strategy to save lives. In this paper, we propose a novel neural-network based algorithm, which we refer to as entropy degradation method (EDM)

During the 21st century, the infinite need for higher-energy beams for basic research in various fields has been a driving force in the advancement of particle accelerators’ technology. The term particle accelerators or particle colliders in modern physics are used to refer to devices that speed up small atomic particles to the velocity of light and maintain them in form of small consistent beams by electromagnetic waves, which pushes the particles in a forward direction (Cousineau

In general sugar fluctuation expansion in the blood is termed as Diabetics. Various diagnosis methods are already carried out to address this issue in real life. Still, it has a research gap to furthermore improve the performance. To receive higher performance two concepts are introduced in this paper. They are Preprocessing and Predictive Analysis. For the concept of Preprocessing several algorithms are used. They are Logistic Regression, Decision Tree Classifier, Linear Discriminant Analysis, KNN Classifier, GNB and SVM. For predictive analysis, Advanced Support Vector Machine is used. While compared with the earlier method our proposed methods provide high accuracy score, high confusion matrix, high classification reports as well as high average & total prediction values.

Expansion of verbal technologies and saturation of communal mass media offers prevailing possibilities to research users’ thinking and emotional states of individuals. Amid this paper, wemention the risk to enhance a stock exchange indicators prediction’s accuracy by mistreatment information concerning mental states of Twitterati. For the investigation of mentalsituations, we tend to usethe lexicon-based approach, which permitstheNorth American nation to gaugethe presence of eight common emotions in additionto 755 million tweets. Neural Networks algorithms and SVM to forecast DJIA and S&P500 indicators are mentioned.

It is clear that with ever improving computational power and endless data, there have been more breakthroughs in Machine Learning. Some practices have clearly emerged as promising while building a neural network. A performance metric to judge the model, is to see if it is in the wrong side of bias or variance. While building a classifier, cases with high bias, and high variance crop up. This paper shall attempt to shed some light on the problem of bias-variance, and how to solve them, with some approaches to perform Regularization