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Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions. The MetaMask wallet for Ethbalance is necessary for BCT transactions. The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods. This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries. The proposed platform’s security measures could mitigate privacy concerns, minimize cyber security risk, and enhance public safety, ultimately promoting the widespread adoption of UAVs. The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication, privacy preservation, confidentiality, integrity, and access control. [ABSTRACT FROM AUTHOR]

In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. AttentionModules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested networkwas built by merging channel and spatial attentionmodules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models. [ABSTRACT FROM AUTHOR]

Currently, breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG)+Logistic Regression hybrid model, (F-score: 0.74) for VGG+Random Forest, (F-score: 0.79) for DenseNet+Random Forest, and (F-score: 0.79) for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images. [ABSTRACT FROM AUTHOR]

Detection of target using infrared images is much demanding now days in different areas of applications. The properties of small target and its presence in the noisy background makes detection task very difficult. So, to mitigate this issue, this paper proposed a novel method using robust principal component (IPNCWLP-RPCA) analysis and non-convex lp-norm minimization. In the existing infrared patch image (IPI) model, because of nuclear norm minimization, the edges in the background were missdetected as a target because of more shrinkage of singular values. To address this issue, proposed method uses non-convex lp-norm (0

In the present infrared target detection system, simultaneously achieving both the target detection performance, as well as, computing efficiency is considered as a big task. To, address the above said issue, a non‐convex triple tensor factorisation is incorporated into the existing infrared patch tensor model in the proposed work. In the proposed model (triple tensor factorisation‐infrared patch tensor), local prior information using linear structure tensor and corner strength is incorporated so that the strong clutters in the background can be easily suppressed and the target can be detected correctly. Finally, the method proposed, was solved by alternating direction method of the multiplier. Large number of experiments were conducted and the results presented in the experiments suggest that the method proposed has shown good performance for background suppression, as well as, for the target detection in the clutter environment, when compared with the other baseline methods. [ABSTRACT FROM AUTHOR]