Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious g...
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The urgent need for health predictions has led to extensive research, but existing methods often fail to cope with the complexity of health data. While traditional methods struggle to capture the temporal and descript...
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The identification and classification of cassava infections are paramount due to their detrimental impact on agricultural productivity. This study conducts a comparative analysis to assess the effectiveness of various...
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ISBN:
(纸本)9798350359299
The identification and classification of cassava infections are paramount due to their detrimental impact on agricultural productivity. This study conducts a comparative analysis to assess the effectiveness of various deep learning techniques in classifying cassava leaf diseases. Transformer-Embedded ResNet, EfficientNetV2 with visual attention, and a mobile-based model are explored to address the challenges posed by an imbalanced dataset. Leveraging deep Convolutional Neural Networks (CNNs) and attention mechanisms, these models exhibit enhanced precision and effectiveness. Evaluations measure accuracy, precision, recall, and F1-score, considering feasibility and computational complexity. Results indicate that the proposed models effectively control cassava infections compared to current methods. This study underscores the precision and efficacy of plant disease identification using deep learning techniques, presenting modern methodologies for thorough evaluation. With agriculture supporting a significant portion of the world's population, AI-powered automation offers solutions to production challenges. AI enhances agricultural precision, monitors crop health, identifies diseases, and forecasts weather conditions, particularly advantageous in addressing manpower shortages. Automated detection is crucial for mitigating agricultural disease risks and ensuring food security. Focusing on the examination of CNNs and neural networks, this study utilizes deep learning techniques to identify cassava leaf diseases using the Kaggle dataset and real-time photographs. Performance evaluation and enhancement are discussed, with a specific focus on the impact of disease on the Thai cassava crop. Experimental trials demonstrate the effectiveness of deep learning in automating cassava disease categorization, particularly in detecting brown streak virus illness, yielding notable F-measure and accuracy. This study advances disease classification and calls for further research in the
In the work at hand we present the design procedure of a waveguide beamformer network that implements the Direct Radiating Arrays' Overlapping Function. Such a design is notorious for its complexity and challenge ...
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mmWave massive MIMO systems will be used extensively in future communications systems to enable the increasing demand for high data rates. In such systems, hybrid precoders are preferred to fully digital precoders to ...
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In the past many years, counterfeit currency has been proliferating on a large scale in India, which is not just increasing black money but also destroying the value and reputation of Indian currency in the global mar...
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It is a challenging task to detect High Impedance Faults (HIF) in distribution networks due to its small, random, and nonlinear characteristics. Utilizing the synchro-waveform data, this paper proposes a data-driven m...
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Urinary Tract Infections (UTIs), predominantly caused by Uro-Pathogenic Escherichia Coli (UPEC), account for 80% of cases, posing a significant health challenge. Traditional methods for bacterial identification, such ...
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In the contemporary digital landscape, ensuring the continuous functionality of computer networks is vital for business and critical infrastructure success. This research project utilizes cutting-edge technologies lik...
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In recent years, blockchain technology has gained immense popularity due to its decentralized and secure nature. However, traditional blockchain systems still face security challenges in terms of confidentiality, inte...
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