To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the d...
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To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.
Automatic speech recognition (ASR) for the Turkish language faces significant challenges due to its agglutinative structure and diverse phonetic variations. In this study, we evaluate the performance of OpenAI's W...
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This paper proposes a novel method for early action prediction based on 3D skeleton data. Our method combines the advantages of graph convolutional networks (GCNs) and adversarial learning to avoid the problems of ins...
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This paper proposes a novel method for early action prediction based on 3D skeleton data. Our method combines the advantages of graph convolutional networks (GCNs) and adversarial learning to avoid the problems of insufficient spatio-temporal feature extraction and difficulty in predicting actions in the early execution stage of actions. In our method, GCNs, which have outstanding performance in the field of action recognition, are used to extract the spatio-temporal features of the skeleton. The model learns how to optimize the feature distribution of partial videos from the features of full videos through adversarial learning. Experiments on two challenging action prediction datasets show that our method performs well on skeleton-based early action prediction. State-of-the-art performance is reported in some observation ratios.
In recent years, we have witnessed the generation of exceptional authentic deepfake images and videos due to the availability of cutting-edge Artificial Intelligence and deep learning techniques. Deepfakes represent s...
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Aiming at the problem of energy consumption optimization in multi-robot path planning, this paper presents a multi-robot path planning algorithm based on optimal energy consumption. First, we improve the traditional A...
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In the MIS (minimally invasive surgery), precise measurement and mastery of human organs is very important, even a slight wobble of instrument can cause a great deal of error. Digital 3D reconstruction technology can ...
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The trajectory tracking performance of flexible joint robots (FJRs) is adversely affected in the presence of measurement noise, unmodelled system dynamics, external disturbances, and parametric variations. This paper ...
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In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive ***,current blockchain systems face the problems of limited throughput,poor scalability,an...
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In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive ***,current blockchain systems face the problems of limited throughput,poor scalability,and high *** to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor *** paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and *** ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the *** allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of *** using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and *** first step involves the safe entry of nodes into the system by verifying their private and public *** next step involves developing an advanced DAG ledger so nodes can start block production and verify *** the third step,a ranking algorithm is developed to separate the nodes created by *** eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological *** a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious *** on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.
In behavior recognition, the accuracy of recognition results is deeply affected by input video processing, which is crucial. Therefore, in order to improve the efficiency and accuracy of SlowFast behavior recognition ...
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Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and ...
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Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w
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