In the domain of cyber security, supporting the flexibility of supply chains against potential dangers is vital. This review dives into the domain of prescient examination, utilizing progressed AI methods including Ca...
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Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work pro...
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In the realm of sentiment analysis, discerning and categorizing emotions encapsulated within textual data stands as a pivotal domain known as emotion classification. With the pervasive influence of online social media...
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In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonsee...
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This paper presents a fuzzy clustering algorithm by incorporating fuzzy entropy for segmentation of 3D brain magnetic resonance (MR) images. Intensity inhomogeneity (IIH) and noise strongly affect brain MR images beca...
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In recent years, the importance of the Internet of Things (IoT) and its applications has increased. IoT networks are diverse, allowing for various real-time applications and making them indispensable to daily life. Ev...
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This work aims to develop a reliable and efficient model that utilizes machine learning techniques to classify speech with high accuracy. The model is built using the Python programming framework Librosa, which extrac...
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In light of recent incidents involving the leakage of private photographs of Hollywood celebrities from iCloud, the need for robust methods to safeguard image content has gained paramount importance. This paper addres...
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In light of recent incidents involving the leakage of private photographs of Hollywood celebrities from iCloud, the need for robust methods to safeguard image content has gained paramount importance. This paper addresses this concern by introducing a novel framework for reversible image editing (RIT) supported by reversible data hiding with encrypted images (RDH-EI) techniques. Unlike traditional approaches vulnerable to hacking, this framework ensures both efficient and secure data embedding while maintaining the original image’s privacy. The framework leverages two established methods: secret writing and knowledge activity. While secret writing is susceptible to hacking due to the complex nature of cipher languages, RDH-EI-supported RIT adopts a more secure approach. It replaces the linguistic content of the original image with the semantics of a different image, rendering the encrypted image visually indistinguishable from a plaintext image. This novel substitution prevents cloud servers from detecting encrypted data, enabling the adoption of reversible data hiding (RDH) methods designed for plaintext images. The proposed framework offers several distinct advantages. Firstly, it ensures the confidentiality of sensitive information by concealing the linguistic content of the original image. Secondly, it supports reversible image editing, enabling the restoration of the original image from the encrypted version without any loss of data. Lastly, the integration of RDH techniques designed for plaintext images empowers the cloud server to embed supplementary data while preserving image quality. Incorporating convolutional neural network (CNN) and generative adversarial network (GAN) models, the framework ensures accurate data extraction and high-quality image restoration. The applications of this concealed knowledge are vast, spanning law enforcement, medical data privacy, and military communication. By addressing limitations of previous methods, it opens new avenues
This research paper analyzes the use of machine learning techniques to predict effective radiated power (ERP) and enhance logistics efficiency in distribution management for smart transportation systems (STS). Three r...
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Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for c...
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