Observing and filming a group of moving actors with a team of aerial robots is a challenging problem that combines elements of multi-robot coordination, coverage, and view planning. A single camera may observe multipl...
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Cloud computing has emerged as significant and paradigm shift in outsourcing storage and computations. data stored in public cloud without encryption is not safe due to many reasons such as insider attacks, flawed har...
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Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and att...
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ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.
Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more c...
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The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including natio...
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Modern criminal investigations rely on forensic evidence management, which necessitates careful handling, safe storage, and precise documenting of the chain of custody. Problems including data manipulation, illegal ac...
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ISBN:
(纸本)9798350349900
Modern criminal investigations rely on forensic evidence management, which necessitates careful handling, safe storage, and precise documenting of the chain of custody. Problems including data manipulation, illegal access, and a lack of transparency plague conventional evidence management systems. In response to these concerns, this study introduces a novel approach to forensic evidence management that makes use of blockchain technology. Nowadays, data plays a crucial role in every stage of the work process. Since data might change, it should be secure. In a diverse organization, we shall handle data capacity and portrayal. data that is vital to a particular organization might be the target of an attack. As the prevalence of cybercrime rises, malicious actors are taking increasingly cunning actions to alter such data. Whatever the case may be, it is significantly impacting the expected scientific confirmation of provenance. Since forensic evidence moves through many stages during scientific examination, it is supposed to be preserved in this manner. With this approach, a report is created and then passes through various levels of delegation, such as a pathology lab, a forensic lab, the police, and so on. To create a clear framework with immutable measurable confirmations, blockchain technology innovation is the best choice. The efficiency of the proposed scheme is evaluated by cross-validation with the conventional security model called Secured Forensic Evidence Handler (SFEH). This paper introduced a novel algorithm called Heterogeneous Key Generation for Forensic data Safety (HKGFDS) to maintain forensic details in a safer manner. By demonstrating how blockchain technology has the ability to revolutionize forensic evidence management methods, this research adds to what is already known. Improved and more trustworthy criminal investigations are possible outcomes of this new line of inquiry and its use in practical forensic contexts. But wider acceptance can't happe
Cancer development is closely linked to the accumulation of mutations in driver genes. Therefore, identification of driver genes is crucial for understanding the molecular basis of cancer. In various types of methods,...
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The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for compa...
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When it comes to realizing immediate quantum advantages in practical applications like machine learning and optimization techniques, numerous methods have emerged as a frontrunner technique. Such algorithms often invo...
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ISBN:
(纸本)9798350325188
When it comes to realizing immediate quantum advantages in practical applications like machine learning and optimization techniques, numerous methods have emerged as a frontrunner technique. Such algorithms often involve quantum circuits for data encoding and the training of Quantum Neural Networks (QNNs) to minimize target functions when applied to tasks involving classical data. Quantum Machine Learning (QML) is a promising area of quantum computing because it may effectively handle difficult learning tasks by taking advantage of the high dimensional Hilbert space to train better representations from limited data. Despite QML's rising popularity, few academic works have addressed the language's security features. There has been a lot of buzz about the promising new discipline of quantum machine learning. Training a parameterized quantum circuit is a common practice in contemporary QML for performing data analysis on classical or quantum datasets. To make accurate predictions on an independent test dataset, state-of-the-art QML techniques first variationally optimize a parameterized quantum circuit using the training dataset. Quantum modeling in QML has also been shown to offer an exponential benefit in sample complexity. In the realms of pattern recognition, machine learning, and data mining, feature dimensionality reduction as a crucial link in the process has become one hot and tough spot. Most academics have gravitated towards this topic because of the difficulty of its studies. The goal of this research is to learn how to implement low loss in the procedure of feature dimension reduction, preserve the integrity of the original data, locate the most effective mapping, and obtain the best possible low-dimensional data. Preliminary findings suggest that QML models may offer advantages over classical models for classical data analysis. In this paper, a thorough investigation into generalization performance in QML is performed. This paper presents a brief survey on
This paper develops the contemporary role of real-time analytics combining IOT-enabled smart grid systems in order to improve energy efficiency, strengthen grid dependability and privacy sustainability. With help of t...
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