Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they st...
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Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they still faced some challenges such as similarity in disease symptoms and irrelevant features *** this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases *** proposed architecture consists of five *** the first step,data augmentation is performed to increase the numbers of training *** the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer *** features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search *** best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification *** proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant *** proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,*** with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.
Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and ...
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Integrating device-to-device (D2D) communication into cellular networks can significantly reduce the transmission burden on base stations (BSs). Besides, integrated sensing and communication (ISAC) is envisioned as a ...
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In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key ena...
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Large Language Models (LLMs) are being employed in various domains to support different tasks. There are many challenges when working with LLMs, such as hallucination and domain knowledge gaps. Retrieval Augmented Gen...
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This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurf...
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We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provi...
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This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configura...
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The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a 'n...
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The present study introduces a novel binary meta-heuristic optimizer, referred to as the Binary Growth Optimizer (BGO), which is specifically developed to address discrete optimization problems. The BGO utilizes trans...
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