Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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Multi-level 3DCNN with?Min-Max Ranking Loss for?Weakly-Supervised Video Anomaly Detection strategy from 3DCNN is proposed to extract the fine lower-level representation of the input video sequences. An efficient temporal dependency encoding is utilized further to capture the sharp change in untrimmed surveillance videos. The proposed method is evaluated on a widely used benchmark anomal

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Vision Transformer-Based Federated Learning for?COVID-19 Detection Using Chest X-Raynlabeled datasets using pre-training, whereas federated learning enables participating clients to jointly train models without disclosing source data outside the originating site. We experimentally establish that our proposed Vision Transformer based Federated Learning architecture outperforms CNN b

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Efficient-Nets and?Their Fuzzy Ensemble: An Approach for?Skin Cancer Classification After that, we combine the prediction probabilities of base classifiers using Choquet fuzzy integral to get the final predicted labels. The proposed architecture is evaluated based on ISIC multi-class skin cancer classification. The rewarded cross-entropy loss-based training regime showcased its su

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A Multi-modal Graph Convolutional Network for?Predicting Human Breast Cancer Prognosiseast cancer, we proposed a novel classification model in this study, that is based on multi-modal graph convolutional networks (MGCN). To extract features, we first build a graph convolutional network (GCN) for individual modalities. And then, we feed the concatenated features generated by GCN into