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Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of the tumor cells. In recent years, advances in deep learning access to medical diagnostic imaging have led to greater accuracy in short time segmenting brain tumors. In this work, a novel approach based on Segmentation-based Kernel Fuzzy C-Mean (SKFCM) with Penguin Search Optimization Algorithm (PeSOA) with an Adaptive Dense Neural Network (ADNN) classifier was implemented. The MRI images are pre-processed using Bright-contrast Dynamic Histogram Equalization (BCDHE) with a weighted median filter and the multi features are extracted with the Modular linear discriminant analysis (MLDA). The Adaptive Dense Neural Network (ADNN) using a unique SKFCM with a machine learning-based Penguin Search Optimization Algorithm was used to segment brain tumors (PeSOA). The effectiveness of the proposed method was estimated based on specificity, accuracy, sensitivity, and tumor area length in vertical and horizontal locations. The proposed approach progresses the overall accuracy of 1.11%, 4.44%, and 6.18% better than CNN, ANN-fuzzy C-means, and R-CNN, respectively. [ABSTRACT FROM AUTHOR]

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]

In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization. [ABSTRACT FROM AUTHOR]

Brain tumor detection, segmentation, and classification are essential in clinical diagnosis and efficient treatment. Researchers have recently shown a greater interest in attaining accurate brain tumor categorization using the Internet of Things (IoT) and machine learning. The rigidity of tumor classification and segmentation in magnetic resonance imaging is due to large data and indistinct boundaries. Hence, in this study, Machine Learning assisted Automatic Brain Tumor Detection Framework (MLABTDF) has been proposed using IoT. Our study includes establishing a deep convolutional neural network (DCNN) for spotting brain tumors from magnetic resonance imageries. This article accommodated technologies of the IoT for helping brain treatment specialists in identifying the need to make surgeries contingent on MR images. The standard medical image dataset has been gathered and experimentally examined to validate the accuracy, efficiency, specificity, sensitivity, optimum automatic recognition for non-tumor and tumor regions, and the model's error rate utilizing statistical construction. This study pays its ability in brain irregularity recognition and analysis in the healthcare sector without humanoid intermediation. Compared to other systems, the experimental results show that the recommended MLABTDF model improves efficiency by 95.7%, segmentation and classification accuracy by 99.9%, specificity by 97.3%, sensitivity by 96.4%, optimal automatic detection by 93.4%, Matthews correlation coefficient ratio by 97.1% and error rate by 10.2%. [ABSTRACT FROM AUTHOR]

As digital cameras become more enhanced and small, CCD sensors can relate to only one color of a pixel. This color mosaic pattern is called as Bayer Pattern(BP) which requires processing to obtain a color image with a higher resolution. Each image pixel that undergoes interpolation has a full color spectrum based on surrounding pixel colors. Here we introduce Adaptive CFA(ACFA) interpolation model. For normal image regions hue technique is used while edge regions adapt the new technique. It is proposed to apply fuzzy logic and fuzzy rule which is based on Genetic Algorithm that uses random local search to enhance the PSNR. Medical image reconstruction by this proposed fuzzy based method outperforms the other medical image reconstruction methods. [ABSTRACT FROM AUTHOR]

This paper presents an effective traffic video surveillance system for detecting moving vehicles in traffic scenes. Moving vehicle identification process on streets is utilized for vehicle tracking, counts, normal speed of every individual vehicle, movement examination, and vehicle classifying targets and might be executed under various situations. In this paper, we develop a novel hybridization of artificial neural network (ANN) and oppositional gravitational search optimization algorithm (ANN–OGSA)-based moving vehicle detection (MVD) system. The proposed system consists of two main phases such as background generation and vehicle detection. Here, at first, we develop an efficient method to generate the background. After the background generation, we detect the moving vehicle using the ANN–OGSA model. To increase the performance of the ANN classifier, we optimally select the weight value using the OGSA algorithm. To prove the effectiveness of the system, we have compared our proposed algorithm with different algorithms and utilized three types of videos for experimental analysis. The precision of the proposed ANN–OGSA method has been improved over 3% and 6% than the existing GSA-ANN and ANN, respectively. Similarly, the GSA-ANN-based MVD system attained the maximum recall of 89%, 91%, and 91% for video 1, video 2, and video 3, respectively. [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]