Virtual voice assistants have transformed the way humans interact with computers, especially with mobile devices. This study examines the latest advancements in speech processing technologies to create a virtual voice...
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The goal of this paper is to use a revolutionary visual cryptography technique to improve the security and contrast of images. This method improves readability and security by enhancing contrast without adding noise t...
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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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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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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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(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
The rapid evolution of deepfake technology in recent years brings forth both promising opportunities and formidable challenges. This research paper conducts a thorough exploration of cutting-edge techniques in deepfak...
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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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Data confidentiality is a critical issue in the digital age, impacting interactions between users and public services and between scientific computing organizations and Cloud and HPC providers. Performance in parallel...
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The internet's explosive expansion has paved the way for a wide range of smartphone applications that use QR codes and digital wallets to facilitate online payments. Security is a major concern for web-based appli...
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Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including objec...
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