Titlebook: Big Data and Security; 5th International Co Yuan Tian,Tinghuai Ma,Muhammad Khurram Khan Conference proceedings 2024 The Editor(s) (if appli

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Analysing Potential of?ResNet for?Transfer Learning with?Stochastic Depthustness of ResNet models with stochastic depth when subjected to a common transfer learning technique: pruning the final layers. Our hypothesis claims that implementing the stochastic depth training approach is a preventive measure against co-adaptation among sequential layers. Consequently, this pr

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A Survey of Research Progresses on Instance Segmentation Based on Deep Learningt of panoramic segmentation technology. It has a wide range of applications in many areas, such as cyber security, intelligent driving, tumor recognition, boundary segmentation, pest, disease recognition, face recognition and beauty enhancement, etc. With the continuous development of deep learning,

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ROMA: Reverse Model-Based Data Augmentation for Offline Reinforcement Learningty that the agent may need to estimate the value of unseen action, which usually results in value overestimation and training instability. The Imitation-learning-based method, which is easy to implement and scale up, bypasses this problem by performing some kind of imitation learning on the dataset

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Deep Learning-Based Attribute Graph Clustering: An Overviewing capabilities of deep learning, attribute graph clustering has emerged as a crucial method for dealing with complex network structures. In the field of network information security, a profound understanding and accurate classification of complex networks are particularly critical. In this article

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Construction of Demand Forecasting Model of Human Resources Professional Structure Based on Deep Lea performance management, and will soon occupy a place in HR planning, training and development, and employee service, and will realize the high intelligence of HR service in the future. Therefore, this paper puts forward a demand forecasting model of HR professional structure based on DL. Firstly, B

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,Teilchengeh?rtete Legierungen,on should characterize the pattern of disciplinary development. This study is an attempt of specialized disciplines development pattern recognition by big data intelligence, and the recognition algorithms can be used for feature recognition in multidisciplinary fields.