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Background: Globally, the most general reason for huge number of passings is Lung disease. The lung malignancy is the most shocking amongst the tumor types and it plays a significant role for the increase of death rate. It is assessed that nearly 1.2 million persons are determined to have this illness and about 1.1 million individuals are losing their lives due to this sickness in every year. The survival rate is superior if the growth is recognized at earlier periods. The premature identification of lung malignant growth isn't a simple task. Various imaging algorithms are available for detecting the lung cancer. Aim: Computer aided diagnosis scheme is more useful for radiologist in detecting and identifying irregularities in advance and more rapidly. The CAD systems usually focus on identifying and detecting the lung nodules. Staging the lung cancer at its detection need to be focused as the treatment is based on the stage of the cancer. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer. Objective: The most important intention of this work is to divide the lung nodule from CT image and classify as tumorous cells in order to identify the cancer's position with greater sensitivity, precision, and accuracy than other strategies. Methods: The primary role is defined as follows (i) for de-noising and edge sharpening of lung image, the curvelet transform is used. (ii) The Fuzzy thresholding technique is used to perform lung image binarization and lung boundary corrections. (iii) Segmentation is performed by using K-means algorithm. (iv) By using convolutional neural network (CNN), different stages of lung nodules such as benign and malignant are identified. Results: The proposed classifier achieves a 97.3 percent accuracy. The proposed approach is helpful in detecting lung cancer in its early stages. The proposed classifier achieved a sensitivity of 98.6 percent and a specificity of 96.1 percent. Conclusion: The results demonstrated that the established algorithms can be used to assist a radiologist in classifying lung images into various stages, thus supporting the radiologist in decision making.

Natural language problems have gained increased attention in the recent times. There are many special purposes, and carefully constructed evaluations driving the NLP research. Problem solving tests offer an interesting alternative to these evaluations. The primary goal of creating these problem solving tests is to evaluate the reading skills, and there by create bank of training materials and ranking procedures to match the existing measures of human performance. Solving Set Theory problems using NLP exposes once such research problem and helps creating an evaluation method for Natural Language Understanding Systems. This paper describes the possibility to challenge these systems to successively push higher performance levels up to an accuracy of 80% with high speed as an added advantage.

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