Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective ...
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Mental health disorders profoundly affect intellectual ability, emotional resilience, and social stability, leading to lasting effects on both individuals and society. Increasing global concern over mental health call...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a mo...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.
Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
A progressive brain disorder, which eventually destroys memory cells, is termed Alzheimer’s Disease (AD). AD causes memory loss and other regular activities. Due to the variations in cytoarchitecture, the categorical...
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A progressive brain disorder, which eventually destroys memory cells, is termed Alzheimer’s Disease (AD). AD causes memory loss and other regular activities. Due to the variations in cytoarchitecture, the categorical labeling of various tissues presents a difficult task in AD classification. For addressing this challenge, this paper proposes a new GELU and SWISH-based Radial Basis Function Network (GS-RBFN)-centric early prediction and classification of AD. For classifying AD into Mild Cognitive Impairment (MCI), AD, and Control Normal (CN), the proposed model deploys image pre-processing, segmentation, morphological operation, data augmentation, image representation extraction, feature selection, and classification steps. Primarily, images are gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Next, by utilizing normalization, skull removal, and spatial smoothing approaches, the images are pre-processed. Then, by using the Brownian Log Scaling Archimedes Optimization-based Watershed Segmentation (BLSAOWS), significant brain tissues are segmented. After that, using morphological operations, the segmented images are enhanced. Next, for obtaining different formations of the segmented images, a data augmentation process is deployed. Subsequently, the image features are extracted, and the best features are chosen utilizing the Base Switch Rule Infimum and Supremum-centric Rock Hyrax Swarm Optimization (BSRISRHSO) algorithm. Lastly, utilizing a new GS-RBFN classifier, the AD is classified. Through the experimental analysis, the proposed model’s efficiency is determined. Thus, the proposed GS-RBFN proficiently predicts AD individuals with an accuracy, precision, and sensitivity of 98.45%, 98.44%, and 98.44%, respectively. The proposed GS-RBFN achieved a less computation time of 14876 ms. Furthermore, the proposed BSRISRHSO obtained a minimum feature selection time of 24012 ms. The Proposed BLSAOWS acquired a high efficiency of 98%. Also, the pro
The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an ini...
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Robust fake speech detection systems are crucial in an era where audio recordings can be easily altered or developed due to advancements in technology. The potential impact of this technology could be devastating due ...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease cond...
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