Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnec...
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Robust exam monitoring solutions are now essential in light of the most notable Covid-19 outbreak and the increasing inclination towards virtual learning. The automated method for supervising online exams that is sugg...
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Voting is a prominent part of the political process, especially in a democratic country like India, where it plays a key role in electing government officials and shaping public policy. Conventional paper-based method...
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The proposed system revolutionizes food oil quality assessment through an intelligent integration of Artificial Intelligence (AI) and Convolutional Neural Networks (CNNs). Overcoming the limitations of traditional met...
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The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. Thi...
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The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contributes to the extensive downstream anal...
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Text-to-speech (TTS) turns written text into spoken words using artificial voices. This uses natural language processing (NLP) and speech synthesis to make audio from text input. TTS has many uses - for people with vi...
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online courses have become more liked recently as a brand-new method of instructing students in remoteness literacy environment. However, as request grows, educational accademies were faced with difficulty of determin...
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Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficien...
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Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical *** few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of *** solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot ***,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of ***,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling ***,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution *** three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.
Due to an exponential rise in the incidence of cyberattacks, there is a greater than ever need for enhanced Intrusion Detection Systems (IDS). In this aspect, the early classification of the attacks is greatly aided b...
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