In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.

This paper presents the threat modeling approach for pervasive environment's security. In pervasive computing, a user might be part of various security domains at any particular instant of time having various authentication mechanisms and different privileges in different security domains. A number of threat modeling approaches and methods have been defined in literature and are in use. However, because of the nature of the pervasive computing and ubiquitous networks, these approaches do not handle the inherent security problems and perspective of pervasive computing. The paper examines in detail the threat modeling and analysis approaches being developed at Microsoft and other methods used for threat modeling. The paper present a novel approach for addressing the threat modeling in pervasive computing and presents the model for threat modeling and risk analysis in pervasive environment.

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Context awareness enables smart service discovery and adaptation for mobile and wireless hosts. The contextual data is acquired from sensors present in the smart space, which may be absent. The inherent noisy nature of wireless environments does not guarantee the gathering of correct data. A history module is thus required in conjunction with existing context-aware systems that overcomes these limitations by predicting the data. We present a modular approach that when coupled with existing context managers will be able to provide user preference on the basis of usage history.

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