Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power...
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Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein datascience and mac...
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The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run ...
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Accurate prediction of above ground biomass (AGB) is critical for monitoring forest health and carbon cycling. It is crucial for understanding and managing forest ecosystems. In this paper, we propose an enhanced fram...
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Potatoes assume a crucial role as a global food crop, contending with challenges posed by various pathogens affecting their growth. Ranked as the third-largest agricultural food crop, potatoes are indispensable for de...
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ISBN:
(纸本)9798350376470
Potatoes assume a crucial role as a global food crop, contending with challenges posed by various pathogens affecting their growth. Ranked as the third-largest agricultural food crop, potatoes are indispensable for delivering essential nutrients. One significant threat to potato health arises from leaf diseases. The accurate detection of these diseases is imperative for effective intervention. This research introduces a novel hybrid model integrating deep learning and transfer learning to predict the risks associated with potato leaf diseases. To train the model, a dataset comprising 3,076 images categorized into seven classes is used. These classes delineate various types of leaf diseases, including those affected by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. The methodology leverages Convolutional Neural Networks (CNNs), recognized for their efficacy in identifying potato leaf diseases through image analysis. To enhance accuracy, Transfer Learning algorithms such as VGG16 and Xception are incorporated. This hybrid model addresses the intricate classification challenges arising from the distinct characteristics and data limitations inherent in potato leaf diseases. Furthermore, data augmentation techniques are employed in conjunction with CNNs to mitigate classification challenges associated with unique features and limited data. The stacking of outputs from these models substantially enhances the accuracy of potato leaf disease detection. In summary, this research endeavors to provide a novel methodology for the precise classification of diverse types of potato leaf diseases, contributing to improved crop management and food security. The proposed model achieves an overall classification accuracy of 94.70%, accompanied by commendable precision, recall, and F1-score values of 93.75%, 93.6%, and 92.75%, respectively. These results validate the model's efficacy in advancing research on potato leaf disease detection, attesting to its
Speech Emotion Recognition (SER) is an emerging field that involves recognizing emotions conveyed in speech. Emotions expressed through speech can greatly impact decision-making. This paper delves into the topic of sp...
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Millions of people worldwide suffer from acne, a common dermatological ailment that frequently causes both physical and psychological discomfort. The prevalence of acne, a common skin condition, poses a significant ch...
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Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimizati...
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Safeguarding visual data is crucial in today's digital world. This paper introduces an image encryption method based on time-seeded randomization, QR decomposition, and Discrete Wavelet Transform (DWT) methods. In...
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This research introduces a Voice-Controlled Car prototype that addresses the existing literature gap in systematic evaluations of voice-controlled systems. The prototype employs Natural Language Processing (NLP) techn...
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