Image segmentation is a classical and basic problem in the domain of computer vision. Due to the fact that fully supervision segmentation methods require dense time-consuming and expensive manual-annotations, lots of ...
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Generating high-quality labels is crucial for self-supervised learning in low-light conditions, where traditional enhancement methods often struggle to balance detail enhancement and color fidelity. This paper present...
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Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machi...
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The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the *** management and prevention of these diseases depend mainly on their...
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The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the *** management and prevention of these diseases depend mainly on their early identification and accurate *** central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango *** study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf *** maintain image quality and consistency,pre-processing techniques were *** then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and *** proposed CNN model was trained and evaluated using our collected *** customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification *** result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease *** proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN *** research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango *** findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.
Prediction systems are an important aspect of intelligent *** engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accur...
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Prediction systems are an important aspect of intelligent *** engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the *** belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,***,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s ***,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of *** balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is *** reasoning process of the SBRB-I model is based on the evidence reasoning(ER)***,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be *** SBRB-I model has good application prospects in prediction systems.
Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-e...
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Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-end *** to the antenna placed at different positions on vehicles,the existing cellular electro-magnetic(EM) wave propagation modelling does not fit properly for V2X direct communication *** order to figure out a feasible understanding of this problem,this paper focuses on the propagation channel analysis in a rural Vehicle-to-Vehicle(V2V) scenario for vehicular communication with antenna position experiments at different *** adopting the ray-tracing algorithm,a rural scenario simulation model is built up via the use of a commercial-off-the-shelf(COTS) EM modelling software package,that computes the path loss received power and delay spread for a given propagation ***,a real-world vehicle measurement campaign was performed to verify the simulation *** simulated and measured receiver power was in good agreement with each other,and the results of this study considered two antenna types located at three different relative heights between the two *** research provides constructive guidance for the V2V antenna characteristics,antenna placement and vehicle communication channel analysis.
Introduction Deep learning(DL),as one of the most transformative technologies in artificial intelligence(AI),is undergoing a pivotal transition from laboratory research to industrial *** at an unprecedented pace,DL is...
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Introduction Deep learning(DL),as one of the most transformative technologies in artificial intelligence(AI),is undergoing a pivotal transition from laboratory research to industrial *** at an unprecedented pace,DL is transcending theoretical and application boundaries to penetrate emerging realworld scenarios such as industrial automation,urban management,and health monitoring,thereby driving a new wave of intelligent *** August 2023,Goldman Sachs estimated that global AI investment will reach US$200 billion by 2025[1].However,the increasing complexity and dynamic nature of application scenarios expose critical challenges in traditional deep learning,including data heterogeneity,insufficient model generalization,computational resource constraints,and privacy-security *** next generation of deep learning methodologies needs to achieve breakthroughs in multimodal fusion,lightweight design,interpretability enhancement,and cross-disciplinary collaborative optimization,in order to develop more efficient,robust,and practically valuable intelligent systems.
This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing *** the proposed method,a clustering method based on Rao...
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This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing *** the proposed method,a clustering method based on Rao-1 algorithm is introduced to optimize anchor box *** clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple *** verify the feasibility and effectiveness of the proposed method,real apple images collected from the Internet are *** with the generic YOLOv3 and Fast Region-based Convolutional Neural Network(Fast R-CNN)algorithms,the proposed method yields the highest mean average precision value for the test ***,it is practical to apply the proposed method for intelligent apple detection and classification tasks.
Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing huma...
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Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
The Internet of Vehicles (IoV) enhances road safety through real-time vehicle-to-vehicle (V2V) communication of traffic messages. However, V2V wireless connectivity poses security and privacy threats, as malicious adv...
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