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FERMixNet: An Occlusion Robust Facial Expression Recognition Model with Facial Mixing Augmentation and Mid-Level Representation Learning

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IEEE Transactions on Affective computing 2024年第2期16卷 639-654页

作者： Huang, Yansong Peng, Junjie Zhang, Wenqiang Zhao, Tong Chen, Gan Tan, Shuhua Yi, Fen Wang, Lu Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Computer Engineering and Science The Shanghai Institute for Advanced Communication and Data Science Shanghai China Fudan University Academy for Engineering and Technology School of Computer Science and Technology Shanghai China YTO Express Co. Ltd. The National Logistics Engineering Laboratory Shanghai China

Facial expressions can provide a better understanding of people's mental status and attitudes towards specific things. However, facial occlusion in real world is an unfavorable phenomenon that greatly affects the performance of facial expression recognition models. Recent works addressing the occlusion problem have primarily relied on attention mechanisms or occlusion discarding methods that focus on non-occluded regions of the face. However, these methods have not achieved a good balance between occlusion robustness and model efficiency. In this paper, we propose a simple and efficient model, called FERMixNet, for occluded facial expression recognition. The model incorporates a novel facial mixing augmentation strategy (FERMix) that generates new training samples by simulating real-world facial occlusion and preserving high expression-related semantic information. By co-training the original and newly generated samples, the model's occlusion robustness is improved without increasing its complexity during inference. Additionally, to further enhance the model's occlusion robustness, we include mid-level representation learning in the network to learn the discriminative non-occluded local features of the samples with low computational cost. Extensive experiments on four public facial occlusion datasets: Occlusion-RAF-DB, Occlusion-FERPlus and FED-RO show that the proposed model achieves state-of-the-art results which demonstrates the good robustness of our method for occluded facial expression recognition. Meanwhile, the proposed model also achieves state-of-the-art results on the in-the-wild facial expression datasets RAF-DB, AffectNet-8, and AffectNet-7. It proves that the proposed model has good application prospects in real world. © 2010-2012 IEEE.

关键词： Face recognition

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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SMOTE-Based deep network with adaptive boosted sooty for the detection and classification of type 2 diabetes mellitus

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Multimedia Tools and Applications 2024年 1-26页

作者： Immadisetty, Phani Kumar Rajabhushanam, C. Computer Science Engineering Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India School of Computing Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India

Type 2 diabetes (T2D) is a prolonged disease caused by abnormal rise in glucose levels due to poor insulin production in the pancreas. However, the detection and classification of this type of disease is very challenging and requires effective techniques for learning the T2D features. Therefore, this study proposes the use of a novel hybridized deep learning-based technique to automatically detect and categorize T2D by effectively learning disease attributes. First, missing value imputation and a normalization-based pre-processing phase are introduced to improve the quality of the data. The Adaptive Boosted Sooty Tern Optimization (Adap-BSTO) approach is then used to select the best features while minimizing complexity. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to verify that the database classes are evenly distributed. Finally, the Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA-BiRNN) technique is proposed to detect and classify the presence and absence of T2D disease accurately. The proposed study is instigated via the Python platform, and two publicly available PIMA Indian and HFD databases are utilized in this study. Accuracy, NPV, kappa score, Mathew's correlation coefficient (MCC), false discovery rate (FDR), and time complexity are among the assessment metrics examined and compared to prior research. For the PIMA Indian dataset, the proposed method obtains an overall accuracy of 99.6%, FDR of 0.0038, kappa of 99.24%, and NPV of 99.6%. For the HFD dataset, the proposed method acquires an overall accuracy of 99.5%, FDR of 0.0052, kappa of 99%, and NPV of 99.4%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

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VTensor:Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework

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Journal of computer science & technology 2023年第5期38卷 1074-1097页

作者：俞峰赵家程崔慧敏冯晓兵薛京灵 Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China School of Computer Science and Engineering University of New South WalesSydney 1466Australia

Tensors are a popular programming interface for developing artificial intelligence(AI)*** refers to the order of placing tensor data in the memory and will affect performance by affecting data locality;therefore the deep neural network library has a convention on the *** AI applications can use arbitrary layouts,and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries,operator developers need to write a lot of layout-related code,which reduces the efficiency of integrating new libraries or developing new ***,the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout,thus losing the opportunity for layout *** on the idea of polymorphism,we propose a layout-agnostic virtual tensor programming interface,namely the VTensor framework,which enables developers to write new operators without caring about the underlying physical layout of *** addition,the VTensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors,and runtime layout-oriented optimizations to globally minimize the number of layout transformation *** results demonstrate that with VTensor,developers can avoid writing layout-dependent *** with TensorFlow,for the 16 operations used in 12 popular networks,VTensor can reduce the lines of code(LOC)of writing a new operation by 47.82%on average,and improve the overall performance by 18.65%on average.

关键词： artificial intelligence(AI)programming layout-oblivious tensor processing

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Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Revolutionizing Potato Farming: A CNN-Powered Approach for E...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Ponnuru, Mahesh Datta Sai Amasala, Likhitha Srm Institute of Science and Technology School of Computing Chennai India Vellore Institute of Technology School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350348460

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector. © 2024 IEEE.

关键词： Agriculture Convolutional Neural Networks (CNNs) Crop protection Food security Early blight Economic stability Late blight Potato disease detection

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of computer science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5791-5804页

作者： Cheng, Hailun Huang, Shenjin Cai, Linghan Xu, Yangfan Wang, Runming Zhang, Yongbing Tsinghua University Institute of Biopharmaceutical and Health Engineering Shenzhen International Graduate School Shenzhen518055 China Harbin Institute of Technology Faculty of Computing Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally employ attention mechanisms to aggregate instance-level features. However, the weakly supervised signal and the imbalanced instance distribution often lead to inaccurate attention localization, compromising the performance and generalization capability of the MIL framework. To address these problems, this paper presents a novel MIL framework called FAMIL that focuses on inaccurate attention and refines them. FAMIL adopts a dual-branch structure and incorporates two innovative online data augmentation strategies: attention-based Mixup (ABMix) and attention-based Masking (ABMask). ABMix emphasizes the significance of positive instances, generalizing Mixup in the MIL scenarios, while ABMask flexibly identifies challenging positive instances to optimize the feature representation. Moreover, these two methods are plug-and-play and can be easily embedded into attention-based MIL methods. Extensive experiments on three public benchmarks demonstrate the superiority of our FAMIL, outperforming current state-of-the-art methods. The test AUC for the binary tumor classification can be up to 92.61% over CAMELYON16. And the AUC over the cancer subtype classification can be up to 93.81% and 98.41% on TCGA-NSCLC and TCGA-RCC datasets, respectively. © 1991-2012 IEEE.

关键词： Image classification

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Investigation of Android Malware Using Deep Learning Approach

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Intelligent Automation & Soft computing 2023年第2期35卷 2413-2429页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

In recent days the usage of android smartphones has increased exten-sively by *** are several applications in different categories bank-ing/ﬁnance,social engineering,education,sports andﬁtness,and many more *** android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source *** the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth *** attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day *** the existing approach,there are various tradi-tional machine learning classiﬁers for building a decision support system with limitations such as low detection rate and less feature *** important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestﬁ*** function parameters of various machine and deep learning classiﬁers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective *** our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.

关键词： Android application permissions multilayer perception relief scoring

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