With the advancement of cloud services, usage of cloud services in every aspect of information technology has surpassed its peak. More and more people are worried that private data would be vulnerable while it is bein...
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Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology...
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Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a uniqu...
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ISBN:
(数字)9798350364866
ISBN:
(纸本)9798350364873
Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a unique framework to build a reliable and straightforward distant prediction model for vector-borne diseases. The suggested strategy incorporates the use of edge computing capabilities to predict the onset and spread of diseases caused by vectors by using real-time data streams from mobile applications. Employing widely used techniques from machine learning, including interpretable models, our system not only achieves high prediction accuracy but also provides insight into the most important factors influencing the spread of disease. Moreover, this approach overcomes the resource constraints essential to edge networks, ensuring the prediction model's dependability and scalability.
The fact that melanoma is defined as an incurable illness in its advanced stages highlights the criticality of early detection and treatment. Various procedures and equipment have been utilised to detect this type of ...
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Underwater fiber optic networks provide hyperlinks that permit communiqué among offshore structures, deep-sea bases, and oil rigs. Despite the growing need for better facts costs, the constrained to be had bandwi...
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Effective treatment regimens are necessary for the long-term management of chronic illnesses, which burden healthcare systems globally. This study investigates using reinforcement learning to optimize chronic illness ...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
Effective treatment regimens are necessary for the long-term management of chronic illnesses, which burden healthcare systems globally. This study investigates using reinforcement learning to optimize chronic illness treatment regimens. Through interactions with their surroundings, agents may acquire optimum decision-making techniques in RL, a branch of ML that aims to maximize cumulative rewards. RL algorithms may modify treatment plans in response to patient feedback and changing medical circumstances by simulating the management of chronic diseases as a series of sequential decision-making steps. This strategy provides flexible and individualized therapies based on each patient’s requirements, which may enhance health outcomes and better use of available resources. In reviewing the literature, the study looks at how RL treats chronic illnesses and highlights this approach’s advantages, difficulties, and possible applications. It also addresses the moral ramifications and issues surrounding using RL-based treatment optimization algorithms in therapeutic contexts. This study highlights RL’s potential to transform chronic illness management by providing tailored, adaptable, and data-driven treatment approaches.
In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enabl...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.
Medical devices connected via internet are open to cyber-attacks, hence the privacy preservation of the user and the devices plays an important role. In this paper, Medical Edge-devices privacy enhanced technique base...
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Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solutio...
Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
In this paper, we propose a modulation-adaptive acoustic gesture recognition system with smartphones (termed, MAA), which can achieve a high recognition accuracy under various modulation schemes and quickly adapt to a...
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