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In all the disease that have existed in mankind lung cancer has emerged as one of the most fata one time and again. Also, it is one of the most common and contributing to deaths among all the cancers. Cases of lung cancer are increasing rapidly. There are about 70,000 cases per year in India. The disease has a tendency to be asymptomatic mostly in its earlier stages thus making it nearly impossible to detect. That’s why early cancer detection plays an important part in saving lives. An early detection can give a patient a better chance to cure and recover. Technology plays a major role in detecting cancer efficiently. Many researchers have proposed different methods based on their studies. In recent times, to use computer technology to solve this problem, several computer-aided diagnosis (CAD) techniques as well as system have been proposed, developed as well as emerged. Those systems use various Machine learning techniques as well as deep learning techniques, there also have been several methods based off of image processing-based techniques to predict the malignancy level of cancer. Here, in this paper, the aim will be focussed onto list, discuss, compare and analyse several methods in image segmentation, feature extraction as well as various techniques to classify and detect lung cancer in there early stages.

Query optimization is challenging task in database. Many different types of techniques used to optimize query. Heuristic Greedy, Iterative Improvement and Ant Colony algorithms is being used to query optimization. Ant colony Algorithm used to find optimal solution for different type of problems. In this paper we modify Ant Colony Algorithm for query optimization and will show the comparison execution time between Heuristic based optimization, Ant Colony Optimization and Modified Ant Colony optimization algorithms. After implementation of said existing algorithms and modified Ant Colony optimization algorithms we found that modified Ant colony taking less computation time as compare to others algorithms.

Medical science in recent times has witnessed the large implications of AI-based IoT approaches that made the clinical process easier than before. However, effective IoT technologies can connect as well as exchange necessary clinical data with other healthcare systems and devices conducted across the vast Internet facilities. With the help of IoT-enabled big data processing technologies, physicians can measure accurate weight, blood pressure, and daily symptoms related to spreading breast cancer cases across the globe. Utilizing IoT is essential for providing proper medical assistance, treatment, and detection at the initial stages within the healthcare environment regulated by the facilities of the Internet of Things. The implementation of IoT-based big data processes food products for supporting the detection and prevention of breast cancer. The study supports in making a critical analysis on the role of IoT in the big data mainly in cancer detection and increasing the quality of food products. The study’s main scope is to employ IoT-enabled big data processing to aid in the identification of breast cancer. However, the research is mainly focused on studying the assistance offered to healthcare professionals and others in identifying the disease effectively. The overall research study is going to investigate the role of IoT in the early detection of breast cancer symptoms. A total of 20 women were studied and certain factors have been identified which are the early symptoms of breast cancer and can potentially cause breast cancer. These include age, family history, breast density, and breast temperature (independent variables). A dependent variable has been selected: probability of breast cancer occurrence. After that, linear regression analysis has been carried out to understand how the independent variables impact the dependent variable. Findings showed that age, family history of cancer, breast density, and breast temperature are some measurable factors for breast cancer detection. The work contributes to a critical investigation of the function of IoT in big data, specifically in cancer detection and improving food product quality. Age acceleration increases the risk of breast cancer development; breast temperature increases slightly during cancer formation, and breast density has a positive impact on cancer development. Lastly, this study has provided a future scope of using IoT in cancer detection and prevention.