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Multi-scale feature extraction and TrasMLP encoder module for ocean HABs segmentation

OCEAN ENGINEERING 2024年第Part1期311卷

作者： Wen, Bi-Yao Wu, Geng-Kun Xu, Jie Zhang, Bei-Ping Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Due to tiny edge and texture details of harmful algae blooms(HABs), existing segmentation networks are not effective for HABs segmentation. In order to solve the above problems, this paper proposes a Multi-scale Feature extraction and TrasMLP encoder Fusion-based network (MFTS). To tackle the complex morphological characteristics and the complex backgrounds of HABs, a TrasMlp module which can effectively identify long-range patterns and adapt network parameters is introduced, enabling accurately parsing of complex algae images. Secondly, the deep convolution module is constructed by combining deep separable convolution with a twochannel attention mechanism to separate the target region from the background. In addition, this paper proposes a Weighted Feature Fusion of Deep Convolution and INRS encoder Module classification network(FDIR) is proposed to quantify the performance of the image segmentation network. The segmentation results on HABs dataset from AICO Lab show that our proposed MFTS model achieves a miou of 90.02%, outperforming the performance of classical segmentation networks such as U-Net and Mask R-CNN. Compared to original HABs dataset, the segmented result shows a 5.1% improvement in the classification accuracy of the FDIR model.

关键词： HABs image segmentation transformer encoder Shift MLP Two-channel attention mechanism Split convolution

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A novel Soft Actor-Critic framework with disjunctive graph embedding and autoencoder mechanism for Job Shop Scheduling Problems

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 614-626页

作者： Zhang, Wenquan Zhao, Fei Yang, Chuntao Du, Chao Feng, Xiaobing Zhang, Yukun Peng, Zhaoxian Mei, Xuesong Xi An Jiao Tong Univ Sch Mech Engn State Key Lab Mfg Syst Engn Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China Qingdao Econ & Technol Dev Zone Haier Water Heater C05 Bldg1 Haier Rd Qingdao Peoples R China Shaanxi Fast Auto Drive Grp Co Ltd 129 West Ave Xian Shaanxi Peoples R China

The Job-Shop Scheduling Problem (JSSP) is a well-established and classic NP-hard combinatorial optimization issue. The quality of its scheduling scheme directly affects the operational efficiency of manufacturing systems. Priority Dispatching Rules (PDRs) are often utilized to address JSSP in real-world contexts, but the process of creating effective PDRs can be daunting and time-consuming. It also necessitates comprehensive domain knowledge, typically resulting in mediocre performance. In this paper, we introduce a novel reinforcement learning (RL) model called Disjunctive Graph Embedding with Autoencoder Mechanism for Job Shop Scheduling Problems (DGEAM-JSSP), designed to automate PDRs learning. Our proposed model confronts the issue using a Graph Neural Network (GNN) to learn node features that encapsulate the spatial structure of the JSSP graph representation. The ensuing policy network is size-agnostic, enabling effective generalization on larger-scale instances. Additionally, we employ a transformer encoder, incorporating parallel encoding and a self-attention mechanism, to successfully recognize long-term dependencies among operations in large-scale scheduling problems. We also implemented an end-to-end training approach using the Soft Actor-Critic (SAC) algorithm to instruct the two modules. Computational experiment results reveal that, with a single training, our agent successfully learns a superior dispatching policy, surpassing PDRs and state-of-the-art RL frameworks specifically tailored for each JSSP instance size in solution quality, as well as OR-Tools in execution speed. Moreover, results from random and benchmark instances illustrate that the uniquely-modeled learned policies have impressive generalization performance on real-world instances and significantly larger-scale scenarios involving up to 2000 operations.

关键词： Soft Actor-Critic Job-shop scheduling Graph embedding transformer encoder

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A Semantic Detection Method for Network Flows With Global and Generalized Nature

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第4期21卷 4713-4730页

作者： Wang, Xiaojuan Luo, Yiqing He, Mingshu Wang, Xinlei Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100088 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100088 Peoples R China

Network threat detection and identification are essential tasks in the defense of cyberspace. However, current network threat detection methods have limitations such as narrow feature extraction, targeted feature effects, and limited generalization performance. Therefore, there is a need for a more comprehensive understanding and description of network behavior. As a result, we propose a global and generalized method for semantic detection of network flow to enhance the definition of flow data and representation of network behavior. To improve the problem of narrow feature range in existing methods, this paper designs three feature embedding methods that represent global, temporal, and local semantic correlations from both temporal and spatial dimensions: global embedding, position embedding, and learning embedding. In order to overcome the problem of existing methods only targeting specific behaviours, this article focuses on constructing global correlation features to replace the detection mode of building an inherent feature set. By utilizing text analysis features, we extract global embedding features containing network flow relationship information by constructing a topology heterogeneous graph between flows and bytes. This is combined with position embedding and learning embedding to complete data detection and behavior classification through input into the transformer encoder. We validated the effectiveness of our method in three scenarios: the Internet, the Internet of Things, and encryption. The final experimental results demonstrated that our proposed method outperformed existing advanced models. Furthermore, after incorporating global embedding representing international correlation relationships, the model's classification accuracy was further improved.

关键词： Feature extraction Semantics Correlation Threat assessment Analytical models transformers Network topology Threat detection semantic analysis topological relationship heterogeneous graph graph convolutional network transformer encoder

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XFall: Domain Adaptive Wi-Fi-Based Fall Detection With Cross-Modal Supervision

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第9期42卷 2457-2471页

作者： Chi, Guoxuan Zhang, Guidong Ding, Xuan Ma, Qiang Yang, Zheng Du, Zhenguo Xiao, Houfei Liu, Zhuang Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ BNRist Beijing 100084 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

关键词： Fall detection Feature extraction Wireless fidelity Sensors Training Wireless sensor networks Wireless communication Domain adaptation fall detection statistical electric field transformer encoder cross-modal supervision

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VAE-Driven Multimodal Fusion for Early Cardiac Disease Detection

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IEEE ACCESS 2024年 12卷 90535-90551页

作者： Wang, Junxin Li, Juanen Wang, Rui Zhou, Xinqi Beijing Normal Univ Fac Arts & Sci Beijing 100875 Peoples R China

This study presents a novel multimodal deep learning model designed to improve early detection and diagnosis of chronic cardiac conditions such as Severe Left Ventricular Hypertrophy (SLVH) and Dilated Left Ventricle (DLV). Leveraging nearly 70,000 medical records from Columbia University Irving Medical Center, the model integrates early-stage CXR structured data and chest X-ray imagery, employing SMOTE to correct data imbalances. The model utilizes the pre-trained EfficientNetB3 for image feature extraction, enhanced with SE-Block and CBAM attention mechanisms, while transformer encoder layers enrich the structured data representation. Notably, it incorporates Variational Autoencoders (VAEs) to encode both types of data into a cohesive low-dimensional latent space, facilitating an innovative multimodal fusion for cardiac disease risk classification. Ablation studies validate the essential role of each component, with VAE-driven feature fusion significantly boosting accuracy and stability (increasing by 5.43% for SLVH and 14.13% for DLV datasets). The model outperforms existing advanced multimodal frameworks, showing a marked improvement in accuracy, recall, precision, and F1 scores. Specifically, it surpasses the leading CLIP model by 1.56% and 0.68% in accuracy for 90-270 day SLVH and DLV datasets, respectively. High AUC values across various disease stages highlight the model's robustness, demonstrating consistently superior performance in disease progression prediction. These results underscore the potential of integrating multimodal data with advanced deep learning techniques to significantly enhance the diagnostic capabilities of medical tools, paving the way for better early cardiac disease interventions and patient outcomes.

关键词： Diseases Deep learning Data models Accuracy Cardiac disease Medical diagnostic imaging X-ray imaging Multisensory integration Encoding Multimodal deep learning early cardiac disease detection SMOTE EfficientNetB3 transformer encoder variational autoencoders embedding medical diagnostics

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transformer embedded spectral-based graph network for facial expression recognition

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第6期15卷 2063-2077页

作者： Jin, Xing Song, Xulin Wu, Xiyin Yan, Wenzhu Nanjing Forestry Univ Coll Informat Sci & Technol 159 Longpan Rd Nanjing 210037 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things 9 Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China Guizhou Univ Coll Big Data & Informat Engn Guiyang 550025 Guizhou Peoples R China Nanjing Normal Univ Sch Artificial Intelligence Sch Comp & Elect Informat 1 Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China

Deep graph convolution networks which exploit relations among facial muscle movements for facial expression recognition (FER) have achieved great success. Due to the limited receptive field, existing graph convolution operations are difficult to model long-range muscle movement relations which plays a crucial role in FER. To alleviate this issue, we introduce the transformer encoder into graph convolution networks, in which the vision transformer enables all facial muscle movements to interact in the global receptive field and model more complex relations. Specifically, we construct facial graph data by cropping regions of interest (ROIs) which are associated with facial action units, and each ROI is represented by the representation of hidden layers from deep auto-encoder. To effectively extract features from the constructed facial graph data, we propose a novel transformer embedded spectral-based graph convolution network (TESGCN), in which the transformer encoder is exploited to interact with complex relations among facial RIOs for FER. Compared to vanilla graph convolution networks, we empirically show the superiority of the proposed model by conducting extensive experiments across four facial expression datasets. Moreover, our proposed TESGCN only has 80K parameters and 0.41MB model size, and achieves comparable results compared to existing lightweight networks.

关键词： Graph convolution Facial expression transformer encoder Action units

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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning

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SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Shahid, Maqsood Hayat, Maqsood Alghamdi, Wajdi Akbar, Shahid Raza, Ali Kadir, Rabiah Abdul Sarker, Mahidur R. Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan 23200 KP Pakistan King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China MY Univ Dept Comp Sci Islamabad 45750 Pakistan Univ Kebangsaan Malaysia Inst Visual Informat Bangi 43600 Selangor Malaysia Univ Diseno Innovac & Tecnol UDIT Ave Alfonso XIII 97 Madrid 28016 Spain

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects. Furthermore, keeping the significant outcomes of the existing computational models, we propose a highly reliable and effective model namely, pACP-HybDeep for the accurate prediction of anticancer peptides. In this model, training peptides are numerically encoded using an attention-based ProtBERT-BFD encoder to extract semantic features along with CTDT-based structural information. Furthermore, a k-nearest neighbor-based binary tree growth (BTG) algorithm is employed to select an optimal feature set from the multi-perspective vector. The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. Our proposed pACP-HybDeep model demonstrated a high training accuracy of 95.33%, and an AUC of 0.97. To validate the generalization capabilities of the model, our pACP-HybDeep model achieved accuracies of 94.92%, 92.26%, and 91.16% on independent datasets Ind-S1, Ind-S2, and Ind-S3, respectively. The demonstrated efficacy, and reliability of the pACP-HybDeep model using test datasets establish it as a valuable tool for researchers in academia and pharmaceutical drug design.

关键词： Anticancer peptides transformer encoder Physiochemical properties Deep Hybrid neural network Binary tree growth feature selection

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Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Mathew, Midhun P. Elayidom, Sudheep Raj, V. P. Jagathy Abubeker, K. M. SOE Cochin Univ Sci & Technol CS Div Cochin Kerala India SMS Cochin Univ Sci & Technol Cochin Kerala India Amal Jyothi Coll Engn Autonomous Kanjirappally Kerala India

In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and pr & eacute;cised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants. Incorporating transformer encoder blocks offers enhanced capability in capturing intricate spatial dependencies within leaf images, promising agricultural sustainability and food security. By providing farmers and farming stakeholders with a reliable tool for rapid disease detection, our model facilitates timely intervention and management practices, ultimately leading to improved crop yields and mitigated economic losses. Through extensive comparative analyses on various datasets and filed tests, the proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98) for Grpae leaves across different datasets including bell pepper and tomato. Furthermore, including DSC layers enhances the computational efficiency of the model while maintaining expressive power, making it well-suited for real-time agricultural applications. The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.

关键词： Agriculture Leaf diseases transformer encoder Depthwise separable convolution Deep learning GPU

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Speech Emotion Recognition in Multimodal Environments with transformer: Arabic and English Audio Datasets

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 581-592页

作者： Mohamed, Esraa A. Koura, Abdelrahim Kayed, Mohammed Beni Suef Univ Fac Sci Bani Suwayf Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt

Speech Emotion Recognition (SER) is a fastdeveloping area of study with a primary goal of automatically identifying and analyzing the emotional states expressed in speech. Emotions are crucial in human communication as they impact the effectiveness and meaning of linguistic expressions. SER aims to create computational approaches and models to detect and interpret emotions from speech signals. One of the primary applications of SER is evident in the field of Human-Computer Interaction (HCI), where it can be used to develop interactive systems that adapt to the user's emotional state based on their voice. This paper investigates the use of speech data for speech emotion recognition. Additionally, we applied a transformation process to convert the speech data into 2D images. Subsequently, we compared the outcomes of this transformation with the original speech data, aligning the comparison with a dataset containing labeled speech samples in both Arabic and English. Our experiments compare three methods: a transformer-based model, a Vision transformer (ViT) based model, and a wave2vec-based model. The transformer model is trained from scratch on two significant audio datasets: the Arabic Natural Audio Dataset (ANAD) and the Toronto Emotional Speech Set (TESS), while the vision transformer is evaluated alongside wave2vec as part of transfer learning. The results are impressive. The transformer model achieved remarkable accuracies of 94% and 99% on ANAD and TESS datasets, respectively. Additionally, ViT demonstrates strong capabilities, achieving accuracies of 88% and 98% on the ANAD and TESS datasets, respectively. To assess the transfer learning potential, we also explore the Wave2Vector model with fine-tuning. However, the findings suggest limited success, achieving only a 56% accuracy rate on the ANAD dataset.

关键词： Speech emotion recognition transformer encoder fine-tuning wav2vec multimodal emotion recognition

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Unified representation aggregated from graph and global features for multi-source data clustering

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Wang, Jiashou Zhao, Guiyan Li, Zhongtai Luo, Hui Yu, Zhongjiang Li, Chun China Tobacco Yunnan Ind Co Ltd Kunming Yunnan Peoples R China Yunnan Daguan Lab Kunming Yunnan Peoples R China

Multi-source data has received increasing attention due to its excellent performance in clustering tasks. However, existing multi-source data clustering methods utilize shallow graph learning methods to model similarity graphs of multi-source data, and ignore the importance of weak connections between samples within the same cluster. Thus, these connections fail to be explored in the global similarity graph of multi-source data. In this paper, we proposed a unified representation aggregated from graph and global features (UGGF) method for multi-source data clustering. Specifically, it utilizes the cross-source graph diffusion process to preserve invariant connections in multi-source similarity graphs and preserve weak connections between samples through their connection relationships between different sources. Furthermore, inspired by self-attention, the encoder of transformers is used to learn multi-source global feature representation. Then, global features and similarity graphs are integrated to comprehensively explore the multi-source data, aiming to obtain a unified representation for the final clustering task. The proposed UGGF method is validated on four benchmark datasets. Extensive experimental results demonstrate the superiority of the proposed method compared with other state-of-the-art multi-source data clustering methods.

关键词： Cross-source graph diffusion transformer encoder GCN DDC multi-source data clustering

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