The detection of Bactrocera oleae (BO), an invasive pest that poses a serious threat to olive tree cultivation, is crucial for ensuring the quality and quantity of olive oil production. Thisstudy explores the applica...
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In recent years, deep learning has revolutionized fieldssuch ascomputer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many re...
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In recent years, deep learning has revolutionized fieldssuch ascomputer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to processsuch data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for taskssuch as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworkssuch as message passing paradigms, spectral and spatial methods, and advanced extensionssuch as hypergraph neural networks and multigraph approaches. This paper also explores domainssuch associal network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizessome critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.
Despite the effectiveness of smart gas meters, many older, non-smart meters are still in use, creating significant challenges and costs associated with upgrading to advanced systems. To address this, developing artifi...
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skin problems are a difficult issue in medicine because there are many different types and they can look very different from each other. Thisstudy is trying to solve this problem by creating a smart system that can a...
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In the contemporary world of highly efficient technological development,fifth-generation technology(5G)isseen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps)...
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In the contemporary world of highly efficient technological development,fifth-generation technology(5G)isseen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applicationssuch as gaming,virtual conferencing,and other interactive electronic *** of this change are not limited to connectivity alone;it urges operators to refine their businessstrategies and offers users better and improved digital *** essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G *** Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine ***,the model performs and yields more accurate *** work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees *** ensemble model shows better prediction performance with the coefficient of determination R squared value equal *** proposed framework issupported by several statistical analyses,such as theWilcoxon signed-rank *** of the benefits of thisstudy are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.
The research focusses on a vision-based human-computer interface, in which we propose a method for recognising and categorising eye motions for the purpose of computer interaction. The goal of thisresearch is to impr...
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The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are ...
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The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROs. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users.
China is the world's largest producer of pigs,but traditional manual prevention,treatment,and diagnosis methods cannot satisfy the demands of the current intensive production *** computer-aided diagnosis(CAD)syste...
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China is the world's largest producer of pigs,but traditional manual prevention,treatment,and diagnosis methods cannot satisfy the demands of the current intensive production *** computer-aided diagnosis(CAD)systems for pigs are dominated by expert systems,which cannot be widely applied because the collection and maintenance of knowledge is difficult,and most of them ignore the effect of multimodal information.A swine disease diagnosis model was proposed in thisstudy,the Text-Guided Fusion Network-swine Diagnosis(TGFN-sD)model,which integrated text case reports and disease *** model integrated the differences and complementary information in the multimodal representation of diseases through the text-guided transformer module such that text case reports could carry the semantic information of disease images for disease ***,it alleviated the phenotypic overlap problem caused by similar diseases in combination with supervised learning and self-supervised *** results revealed that TGFN-sD achieved satisfactory performance on a constructed swine disease image and text dataset(sDT6K)that covered six disease classification datasets with accuracy and F1-score of 94.48%and 94.4%*** accuracies and F1-scores increased by 8.35%and 7.24%compared with those under the unimodal situation and by 2.02%and 1.63%compared with those of the optimal baseline model under the multimodal ***,interpretability analysis revealed that the model focus area was consistent with the habits and rules of the veterinary clinical diagnosis of pigs,indicating the effectiveness of the proposed model and providing new ideas and perspectives for the study of swine disease CAD.
Despite the large body of work on fairness-aware learning for individual modalities like tabular data, images, and text, less work has been done on multimodal data, which fuses various modalities for a comprehensive a...
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Due to the scarcity of high-quality labeled speech emotion data, it is natural to apply transfer learning to emotion recognition. However, transfer learning-basedspeech emotion recognition becomes more challenging be...
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Due to the scarcity of high-quality labeled speech emotion data, it is natural to apply transfer learning to emotion recognition. However, transfer learning-basedspeech emotion recognition becomes more challenging because of the complexity and ambiguity of emotion. Domain adaptation based on maximum mean discrepancy considers marginal alignment of source domain and target domain, but not pay regard to class prior distribution in both domains, which results in the reduction of transfer efficiency. In order to address the problem, thisstudy proposes a novel cross-corpusspeech emotion recognition framework based on local domain adaption. A category-grained discrepancy is used to evaluate the distance between two relevant domains. According to research findings, the generalization ability of the model is enhanced by using the local adaptive *** with global adaptive and non-adaptive methods, the effectiveness of cross-corpusspeech emotion recognition issignificantly improved.
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