Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by B...
详细信息
Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing cro...
详细信息
ISBN:
(纸本)9789819720880
Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. This undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. The proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. The images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. The chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. These layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. The findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. The efficacious application of this model can assist farmers and agricultural specialists in inform
In the Present era, social media information plays an impact on our daily activities. Accurate media information identification is also challenging because of fake or spam information. Social media may receive this in...
详细信息
In the year 2020, the World Health Organization reported that lower back pain constituted the primary cause of disability on a global scale, impacting 619 million individuals. Magnetic resonance imaging (MRI) offers a...
详细信息
Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a v...
详细信息
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient t...
详细信息
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
The optimization of electric vehicle (EV) utilization and efficiency is becoming increasingly essential in the ever-changing landscape of sustainable transportation. Consulting user manuals, online resources, and seek...
详细信息
In present-day journalism, the role of whistleblower or informer has significant value. But sharing critical information sometimes brings havoc to them, and may even hamper their personal safety and security. In the e...
详细信息
People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and...
详细信息
In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring pat...
详细信息
暂无评论