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The diagnosis of brain tumours (BT) is time-consuming and heavily dependent on the radiologists' abilities. Multiple algorithms have been developed for detecting and classifying BT that are both accurate and fast. Recent years have seen an increase in the popularity of deep learning, especially when it comes to developing automated systems that can diagnose and segment BT more accurately and with less time. In this paper, a novel Brain Hexagonal Pattern Network (BHPN) has been proposed to classify the MEG and PET images into normal, benign and malignant tumours. For pre-processing, a bilateral filter is employed to remove noise artifacts from the collected MEG and PET images. To remove the outer cortical and skull region, skull stripping is used, to be implemented to raise the volume of the training datasets. The pre-processed images are segmented using the Otsu threshold algorithm to segment the BT. These segmented tumours are taken as input to the Reversing Hexagonal algorithm to generate the hexagonal feature sets with and without a segmentation mask. In order to categorize tumours into normal, benign and malignant cases, a Spiking Dilated Convolutional Neural Network (SDCNN) classifier system is implemented. The classification accuracy of the Proposed BHPN approach is 99.54%. The Proposed BHPN approach improves the overall accuracy by 1.49%, 2.52%, and 3.93% better than hybrid deep autoencoder (DAE) and Bayesian Fuzzy Clustering (BFC), Deep CNN, and Neutrosophy and Convolutional Neural Network (NS-CNN) respectively.

ABSTRACTThe segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles. This impacts the outcome of tumour detection, causing errors. For addressing this issue, a Feature-Map based Transform Model (FMTM) is introduced to focus on heterogeneous features of input picture to map differences and intensity based on transition Fourier. Unchecked machine learning is used for reliable characteristic map recognition in this mapping process. For the determination of severity and variability, the method of identification depends on symmetry and texture. Learning instances are taught to improve precision using predefined data sets, regardless of loss of labels. The process is recurring until the maximum precision of tumour detection is achieved in low convergence. In this research, FMTM has been applied to brain tumour segmentation to automatically extract feature representations and produce accurate and steady performance because of promising performance made by powerful transition Fourier methods. The suggested model’s performance is shown by the metrics processing time, precision, accuracy, and F1-Score.

Internet of Things assisted vehicular ad-hocnetwork VANET (I-VANET) is one of the preferred technologies to provide fast and reliable communication in smart transportation systems. With the goal of preserving security, I-VANET is characterised by field-programmable gate array-based remote dynamic partial reconfiguration (RDPR) and security providing entity as a washable physical unclonable function (WPUF). In this specific work, an efficient WPUF-based unknown authentication scheme is proposed to identify and avoid malicious vehicles entering into the I-VANET. In addition to that, the proposed work provides a conditional tracing mechanism to hint the vehicles or roadside units that misuse the I-VANET. This system reveals the malicious vehicles to efficiently provide conditional privacy through I-VANET, by enabling a situation where the entities are unknown to each other until they depart from the network. Finally, I2attacks were analysed from possible attacks that can emanate from the availability of RDPR at I-VANET nodes, and WPUF circuit was used to prevent such attacks. The proposed WPUF-based authentication scheme takes only around 120 ms for verifying 100 tokens but other conventional techniques take >900 ms for verifying 100 tokens.