No abstract

In past few decades, a lot of research has been done in the field of machining to improve the quality of machined surface. Out of these machining operations, drilling is widely used in the areas of marine and aerospace for assembly requirements. Titanium alloy Ti-6Al-4V, owing to its vast applications, is regarded as an important material for these industries. Ti-6Al-4V is categorized as difficult to machine material. Based on above stated facts, an experimental study was conducted on the dry drilling of Ti-6Al-4V. The objective of this study was to establish the correlations between drilling parameters such as feed rate and spindle speed, and quality of machined surface which is evaluated in terms of drill hole diameter deviation, exit burr height and surface roughness.

No abstract

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.

No abstract