An accurate detection of abnormal lung nodule detection is very important for effective treatment and surgical procedure to remove the nodules. This paper introduces an efficient deep learning model to classify lung cancer in both left and right lung. It consists of three important stages; preprocessing, lung region detection and abnormal lung nodule detection. Further, a detailed discussion about the performance of the system is given using two benchmark databases; 30 lung CT images taken from the ELCAP dataset and 130 lung CT images taken from the LIDC dataset. An algorithmic framework is first created for the purpose of segmenting left and right lung region by a morphological algorithm after removing the noise by a wiener filter. A well defined deep learning architecture is designed for effective classification or detection of abnormal lung nodule detection by semantic classification. The proposed system is validated on LIDC and ELCAP database and provides an average accuracy of 97.86%.

Food is essential for every living creature. Food consist of many nutrients such as vitamins, minerals, carbohydrates, fats or proteins. These nutrients present in food are partaken by an organism that forms energy for inciting growth and for process of metabolism. Food chemistry mainly deals with a range of chemical processes and synergies between biological and non-organic components. Preservatives are used in the food to increase its usability and retains its consumability. These may have some serious effects. Many of the chemicals VOCs are controlled and eliminated while detecting. These usages can be extended from environmental monitoring to test the emission of materials. These VOC detected can be detected by using gas sensors and the consumed level these VOC was already stored in Rasberry Pi development board and concentration is proportional to the small change in current value