Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requir...
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Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel ap...
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Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep *** this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded *** use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking *** technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video *** use the multi-objective whale optimization algorithm to optimize the entire process and get the best *** performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation *** simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.
Through computer vision and image processing techniques, a set of images from a scene can be reconstructed in 3D to recover a 3D model of the scene, in which dense reconstruction is a crucial part, and most existing a...
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One of the severe health problems and the most common types of heartdisease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart atta...
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One of the severe health problems and the most common types of heartdisease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurswithout any symptoms, it cannot be cured by an intelligent detection *** effective diagnosis and detection of CHD should prevent human ***, intelligent systems employ clinical-based decision support approachesto assist physicians in providing another option for diagnosing and detecting *** paper aims to introduce a heart disease prediction model including phaseslike (i) Feature extraction, (ii) Feature selection, and (iii) Classification. At first,the feature extraction process is carried out, where the features like a time-domainindex, frequency-domain index, geometrical domain features, nonlinear features,WT features, signal energy, skewness, entropy, kurtosis features are extractedfrom the input ECG signal. The curse of dimensionality becomes a severe *** paper provides the solution for this issue by introducing a new ModifiedPrincipal Component Analysis known as Multiple Kernel-based PCA for dimensionality reduction. Furthermore, the dimensionally reduced feature set is thensubjected to a classification process, where the hybrid classifier combining bothRecurrent Neural Network (RNN) and Restricted Boltzmann Machine (RBM)is used. At last, the performance analysis of the adopted scheme is compared overother existing schemes in terms of specific measures.
The healthcare sector is increasingly adopting intelligent systems to address the growing demand of medical services. The system transforms medical services by leveraging advanced computational techniques and healthca...
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Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been intro...
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Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.
The phenomenal growth of big data in social applications and IT software platforms over the last few decades has emphasized the significance of a systematic requirement engineering strategy for analyzing the requireme...
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Large Language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. These models have be...
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For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and ...
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Music surrounds us, and there is no denying that music in visual media can shape and evoke emotions. Yet, understanding how musical preference influences emotions through audio and visual stimuli remains an important ...
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