In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by u...
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With the development of computer vision technology, human pose estimation as an indispensable part of human-computer interaction. Although Light-Weight High-Resolution Network-30 has Lower number of parameters, the pr...
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A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generali...
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Multi-label text classification is a key task in natural language processing, aiming to assign each text to multiple predefined categories simultaneously. Existing neural network models usually learn the same text rep...
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informationtechnology's swift progression has rendered network security a critical component of modern infrastructure. Artificial Intelligence (AI) has revolutionized network security, particularly in threat dete...
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In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the pow...
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In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly...
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In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.
For many years, there has been literature on study abroad, student mobility, and international student exchange;however, the scope & depth of this work has expanded dramatically in the recent two decades. Most of ...
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The most serious disease that can affect paddy plants is blast disease. All over the world, it results in enormous yield losses. A fungus that attacks the plant's leaves, nodes, and grains is the main culprit behi...
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ISBN:
(纸本)9789819726134
The most serious disease that can affect paddy plants is blast disease. All over the world, it results in enormous yield losses. A fungus that attacks the plant's leaves, nodes, and grains is the main culprit behind the disease. Fungicides are frequently used to stop blast disease, but this approach has some drawbacks, including environmental pollution and the development of fungicide-resistant disease strains. An effective tool for modeling and managing complex systems, such as the blast disease in paddy plants, is fuzzy logic. In this study, we investigate the modeling and management of blast disease in paddy plants using fuzzy logic. We'll discuss the input variables, fuzzy sets, rule base, and output variables, among other components, that make up the fuzzy logic system. The various phases of fuzzy logic, including fuzzification, inference, and defuzzification, will also be covered. The advantages of using fuzzy logic to manage blast disease in paddy plants, including its capacity to deal with ambiguous and imprecise data and its potential to integrate with other control systems, will be discussed in the final section. The fungal disease known as rice leaf blast, which is having a devastating effect on rice production and quality throughout the globe, thrives in warm, humid environments. Management of rice production relies on precise and non-destructive diagnostic techniques. The use of hyper spectral imaging technologies for diagnosing plant diseases has much promise. The problem with using hyper spectral data to build an effective illness classification model is that it contains a lot of duplicated information. However, a lack of representative features has been gathered due to the complexity and limited scope of agricultural hyper spectral imaging data collection. This paper discussed the four models DenseNet169-MLP, CNN, EfficientNetB3, and DNN JOA. DenseNet169-MLP achieves the highest accuracy 96.52, precision of 100, F1-score 94.29 compared to other model
Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it ...
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Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.
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