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Left ventricle (LV) segmentation is essential to identify the cardiac functions for treating cardiovascular disorders. Cardiovascular magnetic resonance (CMRI) imaging is a non‐invasive technique for diagnosing cardiovascular diseases. CMRI is widely used to assess the functional integrity of the left and right ventricles for detecting changes in myocardial structure. Clinical parameters of the LV are often retrieved from CMRI scans, such as LV volumes, and ejection fraction. Moreover, manually segmenting cardiac diseases and evaluating such functions is time‐consuming and difficult for medical professionals. Deep learning networks require a lot of time, cost, and knowledge. To overcome this issue, a novel Edge and Shape feature‐based Fully Convolutional Neural Network (ES‐FCN) has been proposed for automatic LV segmentation. The ES‐FCN model segments MRI images based on edge maps instead of using gray‐scale images, which accelerates the performance of the FCN. The fuzzy‐based canny edge detection algorithm leverages fuzzy logic to detect structural changes in the LV and generate binary images. Additionally, binary‐valued kernels are used for convolution operations, where the binary values are influenced by biases derived from edge map shape descriptors. In ES‐FCN, bias values are learned from ground truth segmentation. The proposed ES‐FCN model achieves the Jaccard indices of 0.9484 ± 0.0188 for the ACDC dataset and 0.9476 ± 0.0237 for the LVSC dataset, and the dice index of 0.9319 ± 0.0188 for the ACDC dataset and 0.9314 ± 0.0237 for LVSC dataset respectively. The experimental results also reveal that the proposed ES‐FCN model is faster and requires minimal resources compared to state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

Summary: The prevalence of tomato leaf diseases should be diagnosed in early‐stage to prevent spoilage of the entire field. Manually checking tomato diseases consumes more time and is labor‐intensive. In modern agriculture, machine and deep learning‐based disease identification techniques have been developed to effectively classify diseases. Most of the existing methods are inappropriate for horticulture due to their incompetence in handling the complex backgrounds of the image. In this article, a novel segmentation and classification algorithm is proposed for detecting tomato leaf diseases with complex background interference based on leaf segmentation fuzzy CNN (LSFCNN) and ant colony‐based mask RCNN (AC‐MRCNN). Foremostly the collected images are annotated and enhanced for further processing. Then the novel LSFCNN is implemented to separate the tomato leaf in a complex background. For classification, AC‐MRCNN is developed, which masks the disease spot and recognizes the diseases. Herein ant colony optimization algorithm is utilized to optimize the mask RCNN to increase the flow of information and gradients of the network. Over 14,817 uniform and complex background images are collected to train the model. The proposed method is profoundly effective for quite challenging background leaf disease classification, with an accuracy of 97.66% of eight diseases and one healthy class. [ABSTRACT FROM AUTHOR]

Internet of Things assisted vehicular ad‐hoc network 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, I2 attacks 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. [ABSTRACT FROM AUTHOR]