Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed to McCulloch's Kapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.
Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning‐based solution to the agricultural community, which helped farmers to identify the diseases at the early stages. In this article, our focus is on grape diseases, and proposes a novel framework to identify and classify the selected diseases at the early stages. A deep learning‐based solution is embedded into a conventional architecture for optimal performance. Three primary steps are involved; (a) feature extraction after applying transfer learning on pre‐trained deep models, AlexNet and ResNet101, (b) selection of best features using proposed Yager Entropy along with Kurtosis (YEaK) technique, (c) fusion of strong features using proposed parallel approach and later subject to classification step using least squared support vector machine (LS‐SVM). The simulations are performed on infected grape leaves obtained from the plant village dataset to achieving an accuracy of 99%. From the simulation results, we sincerely believe that our proposed approach performed exceptionally compared to several existing methods.
Traffic Congestion is becoming a huge issue in the cities of both developing and developed countries. One prominent solution is to solve this issue in terms of smart cities. In smart cities, all end points including people, houses, buildings, and vehicles are connected to each other through some networking technology. The most considered technologies include Internet of Things (IOT) and adhoc networks. The smart city project can also be applied through the combination of IOT and adhoc network. The literature studies show that a very rare work is done on the combination of traffic congestion, IOT and adhoc networks in terms of smart cities. This paper presents an overview of this technology which will help the readers to consider these technologies related to the smart city-based traffic management.
The Vehicular Ad Hoc Network (VANET) plays a vital role in the development of smart cities, especially in ensuring vehicles' safety on roads. However, VANET wireless-based networks face some challenges such as security, stability, communication, and reliability. To resolve these issues, we propose a fuzzy cluster head selection scheme in Cognitive Radio (CR) VANET, which uses the CR technology for the spectrum sensing algorithm. In this technology, the free spectrums of the primary user are utilized by secondary users without any correlation. Moreover, we have considered some input parameters such as vehicles' average velocity, distance, network connectivity level, lane weight and trustworthiness for the fuzzy system based CR VANET in this research. The selected cluster head provides stability and reliability to the cluster compared to the state of art techniques. Extensive experiments were conducted in order to evaluate the effectiveness of the proposed approach. However, simulation results authenticate more stable and secure cluster formation using the proposed fuzzy logic based CR VANET.
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