Vehicular networks that consist of interlinked automobiles and transportation network are prone to cyberattacks due to the increased use of software and the presence of wireless interfaces. To counter these threats, i...
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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...
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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
The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes i...
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作者:
Ghanem, Sahar I.
Computer Science and Artificial Intelligence Faculty Artificial Intelligence and Machine Learning Department Egypt
In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and per...
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Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant struc...
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The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimi...
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This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short 'a' sound (\...
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This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old...
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作者:
Suman, SaurabhPacharaney, UtkarshaGote, Pradnyawant M.
Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India
Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind F...
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This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare...
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