Currently, lung cancer is a very common form of cancer. This is because many people are chain smokers nowadays, and many are affected due to their work hazards. The pollution level in modern cities is also a major cause of this type of cancer. This model is built to predict the chances of the occurrence of lung cancer in an individual with the help of certain conditions. The acquired dataset used in this study contains multiple features, but not all are necessary for predicting the risk of lung cancer. Hence, an embedding feature importance model Light Gradient Boosting Machine (LGBM) is used to find the impact of every feature, and the model had trained using the features with maximum influence. The dataset has been divided into two parts for training and testing the model. The models achieve a k-fold mean accuracy of 97.63% and above with all the features and more than 93% on the reduced features for all the deployed models. The models are developed based on a resource-constrained device perspective over the reduced features low resource dataset and use an algorithm to measure the execution time taken for every model to run and complete its prediction after fitting with the respective classifiers. The model developed on medical data should have maximum accuracy and is necessary for time efficiency that reflects on all the deployed models with stability and efficacy, indicating the robustness and non-overfitted model.

Multicast communication in IoT is prevalent in many applications like smart lighting, firmware update etc. In such applications a single multicast message is sent to all the group members. The group members respond with unicast messages. Multicasting saves energy, decreases network traffic by reducing the number of messages in the network. Security in multicasting is of the utmost importance due to its deployment in many sensitive applications and inherent properties of IoT network. In the literature, various solutions have been proposed to secure group communication. However, there is still no suitable approach that satisfies the secure multicasting need of IoT. In this paper we propose a DTLS (Datagram Transport Layer Security) based secure group communication scheme. The proposed scheme is lightweight, scalable, and robust against member compromise. Moreover, the proposed scheme authenticates each group member and is also suitable for dynamic groups. The simulation results prove that the proposed scheme is more suitable for secure IoT framework in terms of energy and memory requirement than other related schemes.