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Journal articles on the topic 'Attention based models'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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1

Zang, Yubin, Zhenming Yu, Kun Xu, Minghua Chen, Sigang Yang, and Hongwei Chen. "Fiber communication receiver models based on the multi-head attention mechanism." Chinese Optics Letters 21, no. 3 (2023): 030602. http://dx.doi.org/10.3788/col202321.030602.

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Qin, Chu-Xiong, and Dan Qu. "Towards Understanding Attention-Based Speech Recognition Models." IEEE Access 8 (2020): 24358–69. http://dx.doi.org/10.1109/access.2020.2970758.

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Cha, Peter, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, and Eun-Ah Kim. "Attention-based quantum tomography." Machine Learning: Science and Technology 3, no. 1 (2021): 01LT01. http://dx.doi.org/10.1088/2632-2153/ac362b.

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Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Va

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Thapa, Krishu K., Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, and Ananth Kalyanaraman. "Attention-Based Models for Snow-Water Equivalent Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22969–75. http://dx.doi.org/10.1609/aaai.v38i21.30337.

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Snow Water-Equivalent (SWE)—the amount of water available if snowpack is melted—is a key decision variable used by water management agencies to make irrigation, flood control, power generation, and drought management decisions. SWE values vary spatiotemporally—affected by weather, topography, and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporal complete data. While recent efforts have explored machine learning (ML) for SW

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Fallahnejad, Zohreh, and Hamid Beigy. "Attention-based skill translation models for expert finding." Expert Systems with Applications 193 (May 2022): 116433. http://dx.doi.org/10.1016/j.eswa.2021.116433.

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Steelman, Kelly S., Jason S. McCarley, and Christopher D. Wickens. "Theory-based Models of Attention in Visual Workspaces." International Journal of Human–Computer Interaction 33, no. 1 (2016): 35–43. http://dx.doi.org/10.1080/10447318.2016.1232228.

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王, 紫阳. "Wind Speed Prediction Based on Attention-Combined Models." Artificial Intelligence and Robotics Research 14, no. 02 (2025): 389–96. https://doi.org/10.12677/airr.2025.142038.

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AlOmar, Ban, Zouheir Trabelsi, and Firas Saidi. "Attention-Based Deep Learning Modelling for Intrusion Detection." European Conference on Cyber Warfare and Security 22, no. 1 (2023): 22–32. http://dx.doi.org/10.34190/eccws.22.1.1172.

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Cyber-attacks are becoming increasingly sophisticated, posing more significant challenges to traditional intrusion detection methods. The inability to prevent intrusions could compromise the credibility of security services, thereby putting data confidentiality, integrity, and availability at risk. In response to this problem, research has been conducted to apply deep learning (DL) models to intrusion detection, leveraging the new era of AI and the proven efficiency of DL in many fields. This study proposes a new intrusion detection system (IDS) based on DL, utilizing attention-based long shor

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Tangsali, Rahul, Swapnil Chhatre, Soham Naik, Pranav Bhagwat, and Geetanjali Kale. "Evaluating Performances of Attention-Based Merge Architecture Models for Image Captioning in Indian Languages." Journal of Image and Graphics 11, no. 3 (2023): 294–301. http://dx.doi.org/10.18178/joig.11.3.294-301.

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Image captioning is a growing topic of research in which numerous advancements have been made in the past few years. Deep learning methods have been used extensively for generating textual descriptions of image data. In addition, attention-based image captioning mechanisms have also been proposed, which give state-ofthe- art results in image captioning. However, many applications and analyses of these methodologies have not been made in the case of languages from the Indian subcontinent. This paper presents attention-based merge architecture models to achieve accurate captions of images in fou

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Kong, Phutphalla, Matei Mancas, Bernard Gosselin, and Kimtho Po. "DeepRare: Generic Unsupervised Visual Attention Models." Electronics 11, no. 11 (2022): 1696. http://dx.doi.org/10.3390/electronics11111696.

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Visual attention selects data considered as “interesting” by humans, and it is modeled in the field of engineering by feature-engineered methods finding contrasted/surprising/unusual image data. Deep learning drastically improved the models efficiency on the main benchmark datasets. However, Deep Neural Networks-based (DNN-based) models are counterintuitive: surprising or unusual data are by definition difficult to learn because of their low occurrence probability. In reality, DNN-based models mainly learn top-down features such as faces, text, people, or animals which usually attract human at

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Fei Wang, Fei Wang, and Haijun Zhang Fei Wang. "Multiscale Convolutional Attention-based Residual Network Expression Recognition." 網際網路技術學刊 24, no. 5 (2023): 1169–75. http://dx.doi.org/10.53106/160792642023092405015.

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<p>Expression recognition has wide application in the fields of distance education and clinical medicine. In response to the problems of insufficient feature extraction ability of expression recognition models in current research, and the deeper the depth of the model, the more serious the loss of useful information, a residual network model with multi-scale convolutional attention is proposed. This model mainly takes the residual network as the main body, adds normalization layer and channel attention mechanism, so as to extract useful image information at multiple scales, and incorpora

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Hashemi, Seyyed Mohammad Reza. "A Survey of Visual Attention Models." Ciência e Natura 37 (December 19, 2015): 297. http://dx.doi.org/10.5902/2179460x20786.

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The present paper surveys visual attention models, showing factors’ categorization. It also studies bottom-up models in comparison to top-to-down, spatial models compared to spatial-temporal ones, obvious attention against the hidden one, and space-based models against the object-based ones. It categorizes some challenging model issues, including biological calculations, correlation with the set of eye-movement data, as well as bottom-up and top-to-down topics, explaining each in details.

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Lee, Soohyun, and Jongyoul Park. "Attention Map-Based Automatic Masking for Object Swapping in Diffusion Models." Journal of KIISE 52, no. 4 (2025): 284–92. https://doi.org/10.5626/jok.2025.52.4.284.

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Yue, Wang, and Li Lei. "Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification." Information 26, no. 3 (2023): 117–62. http://dx.doi.org/10.47880/inf2603-02.

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With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in

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Sharada, Gupta, and N. Eshwarappa Murundi. "Breast cancer detection through attention based feature integration model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2254–64. https://doi.org/10.11591/ijai.v13.i2.pp2254-2264.

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Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM)

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Singh, Sushant, and Ausif Mahmood. "CacheFormer: High-Attention-Based Segment Caching." AI 6, no. 4 (2025): 85. https://doi.org/10.3390/ai6040085.

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Efficiently handling long contexts in transformer-based language models with low perplexity is an active area of research. Numerous recent approaches like Linformer, Longformer, Performer, and Structured state space models (SSMs), have not fully resolved this problem. All these models strive to reduce the quadratic time complexity of the attention mechanism while minimizing the loss in quality due to the effective compression of the long context. Inspired by the cache and virtual memory principle in computers, where in case of a cache miss, not only the needed data are retrieved from the memor

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Wenjuan Xiao, Wenjuan Xiao, and Xiaoming Wang Wenjuan Xiao. "Attention Mechanism Based Spatial-Temporal Graph Convolution Network for Traffic Prediction." 電腦學刊 35, no. 4 (2024): 093–108. http://dx.doi.org/10.53106/199115992024083504007.

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<p>Considering the complexity of traffic systems and the challenges brought by various factors in traffic prediction, we propose a spatial-temporal graph convolutional neural network based on attention mechanism (AMSTGCN) to adapt to these dynamic changes and improve prediction accuracy. The model combines the spatial feature extraction capability of graph attention network (GAT) and the dynamic correlation learning capability of attention mechanism. By introducing the attention mechanism, the network can adaptively focus on the dependencies between different time steps and different nod

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Zampierin, Luca, and Flavius Frasincar. "An Unsupervised Approach Based on Attentional Neural Models for Aspect-Based Sentiment Classification." ACM SIGAPP Applied Computing Review 25, no. 2 (2025): 5–17. https://doi.org/10.1145/3746626.3746627.

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Due to the vast amount of reviews available on the Web, in the past decades, a growing share of work has focused on sentiment analysis. Aspect-based sentiment classification is the subtask that seeks to detect the sentiment expressed by the content creators towards a defined target within a sentence. This paper introduces three novel unsupervised attentional neural network models for aspect-based sentiment classification, and tests them on English restaurant reviews. The first model employs an autoencoder-like structure to learn a sentiment embedding matrix where each row of the matrix represe

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Nazari, Sana, and Rafael Garcia. "Going Smaller: Attention-based models for automated melanoma diagnosis." Computers in Biology and Medicine 185 (February 2025): 109492. https://doi.org/10.1016/j.compbiomed.2024.109492.

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Zhou, Qifeng, Xiang Liu, and Qing Wang. "Interpretable duplicate question detection models based on attention mechanism." Information Sciences 543 (January 2021): 259–72. http://dx.doi.org/10.1016/j.ins.2020.07.048.

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Israr, Huma, Safdar Abbas Khan, Muhammad Ali Tahir, Muhammad Khuram Shahzad, Muneer Ahmad, and Jasni Mohamad Zain. "Neural Machine Translation Models with Attention-Based Dropout Layer." Computers, Materials & Continua 75, no. 2 (2023): 2981–3009. http://dx.doi.org/10.32604/cmc.2023.035814.

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Yang, Zhifei, Wenmin Li, Fei Gao, and Qiaoyan Wen. "FAPA: Transferable Adversarial Attacks Based on Foreground Attention." Security and Communication Networks 2022 (October 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/4447307.

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Deep learning models are vulnerable to attacks by adversarial examples. However, current studies are mainly limited to generating adversarial examples for specific models, and the migration of adversarial examples between different models is rarely studied. At the same time, in only studies, it is not considered that adding disturbance to the position of the image can improve the migration of adversarial examples better. As the main part of the picture, the model should give more weight to the foreground information in the recognition. Will adding more perturbations to the foreground informati

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He, Ruifeng, Mingtian Xie, and Aixing He. "Video anomaly detection based on hybrid attention mechanism." Applied and Computational Engineering 57, no. 1 (2024): 212–17. http://dx.doi.org/10.54254/2755-2721/57/20241336.

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To improve the ability of video anomaly detection models to extract normal behavior features of samples and suppress abnormal behaviors, this paper proposes an unsupervised video anomaly detection model, which takes advantage of spatio-temporal feature fusion, storage module, attention mechanism, and 3D autoencoder model. The model utilizes autoencoder to capture scene feature maps to enhance anomaly feature extraction. These maps are merged with the original video frames, forming fundamental units constituting continuous sequences serving as the model's input. Moreover, the attention mechanis

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Rosenberg, Monica D., Wei-Ting Hsu, Dustin Scheinost, R. Todd Constable, and Marvin M. Chun. "Connectome-based Models Predict Separable Components of Attention in Novel Individuals." Journal of Cognitive Neuroscience 30, no. 2 (2018): 160–73. http://dx.doi.org/10.1162/jocn_a_01197.

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Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executiv

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Rayeesa, Mehmood, Bashir Rumaan, and J. Giri Kaiser. "Deep Generative Models: A Review." Indian Journal of Science and Technology 16, no. 7 (2023): 460–67. https://doi.org/10.17485/IJST/v16i7.2296.

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ABSTRACT <strong>Objectives:</strong> To provide insight into deep generative models and review the most prominent and efficient deep generative models, including Variational Auto-encoder (VAE) and Generative Adversarial Networks (GANs). <strong>Methods:</strong> We provide a comprehensive overview of VAEs and GANs along with their advantages and disadvantages. This paper also surveys the recently introduced Attention-based GANs and the most recently introduced Transformer based GANs. <strong>Findings:</strong> GANs have been intensively researched because of their sig

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Guo, Yuxi. "Interpretability analysis in transformers based on attention visualization." Applied and Computational Engineering 76, no. 1 (2024): 92–102. http://dx.doi.org/10.54254/2755-2721/76/20240571.

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Self-attention is the core idea of the transformer, a kind of special structure for models to understand sentences and texts. Transformer is growing fast, but the model's internal unknowns are still out of control. In this work, the research visualizes self-attention and observes those self-attentions in some transformers. Through observation, there are five types of self-attention connections. The research classifies them as Parallel self-attention head, Radioactive self-attention head, Homogeneous self-attention head, X-type self-attention head, and Compound self-attention head. The Parallel

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Wang, Lei, Ed X. Wu, and Fei Chen. "EEG-based auditory attention decoding using speech-level-based segmented computational models." Journal of Neural Engineering 18, no. 4 (2021): 046066. http://dx.doi.org/10.1088/1741-2552/abfeba.

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Kramer, Arthur F., and Andrew Jacobson. "A comparison of Space-Based and Object-Based Models of Visual Attention." Proceedings of the Human Factors Society Annual Meeting 34, no. 19 (1990): 1489–93. http://dx.doi.org/10.1177/154193129003401915.

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Yeom, Hong-gi, and Kyung-min An. "A Simplified Query-Only Attention for Encoder-Based Transformer Models." Applied Sciences 14, no. 19 (2024): 8646. http://dx.doi.org/10.3390/app14198646.

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Transformer models have revolutionized fields like Natural Language Processing (NLP) by enabling machines to accurately understand and generate human language. However, these models’ inherent complexity and limited interpretability pose barriers to their broader adoption. To address these challenges, we propose a simplified query-only attention mechanism specifically for encoder-based transformer models to reduce complexity and improve interpretability. Unlike conventional attention mechanisms, which rely on query (Q), key (K), and value (V) vectors, our method uses only the Q vector for atten

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Hanafi, Hanafi, Andri Pranolo, Yingchi Mao, Taqwa Hariguna, Leonel Hernandez, and Nanang Fitriana Kurniawan. "IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism." International Journal of Advances in Intelligent Informatics 9, no. 1 (2023): 121. http://dx.doi.org/10.26555/ijain.v9i1.942.

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An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that

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Sun, Wenhao, Xue-Mei Dong, Benlei Cui, and Jingqun Tang. "Attentive Eraser: Unleashing Diffusion Model’s Object Removal Potential via Self-Attention Redirection Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20734–42. https://doi.org/10.1609/aaai.v39i19.34285.

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Recently, diffusion models have emerged as promising newcomers in the field of generative models, shining brightly in image generation. However, when employed for object removal tasks, they still encounter issues such as generating random artifacts and the incapacity to repaint foreground object areas with appropriate content after removal. To tackle these problems, we propose Attentive Eraser, a tuning-free method to empower pre-trained diffusion models for stable and effective object removal. Firstly, in light of the observation that the self-attention maps influence the structure and shape

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Kristensen, Terje. "Towards Spike based Models of Visual Attention in the Brain." International Journal of Adaptive, Resilient and Autonomic Systems 6, no. 2 (2015): 117–38. http://dx.doi.org/10.4018/ijaras.2015070106.

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A numerical solution of Hodgkin Huxley equations is presented to simulate the spiking behavior of a biological neuron. The solution is illustrated by building a graphical chart interface to finely tune the behavior of the neuron under different stimulations. In addition, a Multi-Agent System (MAS) has been developed to simulate the Visual Attention Network Model of the brain. Tasks are assigned to the agents according to the Attention Network Theory, developed by neuroscientists. A sequential communication model based on simple objects has been constructed, aiming to show the relations and the

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Chandrasekaran, Ganesh, Mandalapu Kalpana Chowdary, Jyothi Chinna Babu, Ajmeera Kiran, Kotthuru Anil Kumar, and Seifedine Kadry. "Deep learning-based attention models for sarcasm detection in text." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6786. http://dx.doi.org/10.11591/ijece.v14i6.pp6786-6796.

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Finding sarcastic statements has recently drawn a lot of curiosity in social media, mainly because sarcastic tweets may include favorable phrases that fill in unattractive or undesirable attributes. As the internet becomes increasingly ingrained in our daily lives, many multimedia information is being produced online. Much of the information recorded mostly on the internet is textual data. It is crucial to comprehend people's sentiments. However, sarcastic content will hinder the effectiveness of sentiment analysis systems. Correctly identifying sarcasm and correctly predicting people's motive

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Sun, Xinhao. "Application of Attention-Based LSTM Hybrid Models for Stock Price Prediction." Advances in Economics, Management and Political Sciences 104, no. 1 (2024): 46–60. http://dx.doi.org/10.54254/2754-1169/104/2024ed0152.

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The stock market plays a pivotal role in the national economy, while the application of artificial intelligence (AI) in stock price prediction has gained traction. This paper evalu-ates the performance of five advanced deep learning (DL) models: Long Short-Term Memory (LSTM), Self-attention, Convolutional Neural Network-LSTM with attention (CNN-LSTM-attention), Gated Recurrent Unit-LSTM with attention (GRU-LSTM-attention), and CNN-Bidirectional LSTM-GRU with attention (CNN-BiLSTM-GRU-attention), utilizing a decade of data on Amazons closing prices. Our results show that the CNN-BiLSTM-GRU-atte

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Kamalov, Firuz, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, and Pavel Matrenin. "Attention-Based Load Forecasting with Bidirectional Finetuning." Energies 17, no. 18 (2024): 4699. http://dx.doi.org/10.3390/en17184699.

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Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting thi

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Zheng, Guoqiang, Tianle Zhao, and Yaohui Liu. "Cloud Removal in the Tibetan Plateau Region Based on Self-Attention and Local-Attention Models." Sensors 24, no. 23 (2024): 7848. https://doi.org/10.3390/s24237848.

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Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau, as a sensitive region to climate change, plays a crucial role in the East Asian water cycle and regional climate due to its snow cover. However, the rich ice and snow resources, rapid snow condition changes, and active atmospheric convection in the plateau as well as its surrounding mountainous areas, make optical remote sensing prone to cloud in

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Alsayadi, Hamzah A., Abdelaziz A. Abdelhamid, Islam Hegazy, and Zaki T. Fayed. "Non-diacritized Arabic speech recognition based on CNN-LSTM and attention-based models." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 6207–19. http://dx.doi.org/10.3233/jifs-202841.

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Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espres

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Guang, Jiahe, Xingrui He, Zeng Li, and Shiyu He. "Road Pothole Detection Model Based on Local Attention Resnet18-CNN-LSTM." Theoretical and Natural Science 42, no. 1 (2024): 131–38. http://dx.doi.org/10.54254/2753-8818/42/20240669.

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Abstract. In response to the low detection accuracy and slow speed of existing road pothole detection methods, a road pothole classification detection model based on local attention Resnet18-CNN-LSTM (Long Short-Term Memory network) is proposed. On the basis of Resnet18, a local attention mechanism and a CNN-LSTM combined model are added to propose a road pothole detection model based on local attention Resnet18-CNN-LSTM. The local attention mechanism is used to accurately extract specific target feature values, CNN is used to extract the spatial features of the input data, and LSTM enhances t

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Shin, Yehjin, Jeongwhan Choi, Hyowon Wi, and Noseong Park. "An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8984–92. http://dx.doi.org/10.1609/aaai.v38i8.28747.

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Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden representations becoming similar to tokens. In the SR domain, we, for the first time, show that the same problem occurs. We present pioneering investigations that reveal the low-pass filtering nature of self-attention in the SR, which causes oversmoothing. To this end, we propose a novel method called Beyond Self-Attention for Sequential Recommendation

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Zhou, Jiawei. "Predicting Stock Price by Using Attention-Based Hybrid LSTM Model." Asian Journal of Basic Science & Research 06, no. 02 (2024): 145–58. http://dx.doi.org/10.38177/ajbsr.2024.6211.

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The financial markets are inherently complex and dynamic, characterized by high volatility and the influence of numerous factors. Predicting stock price trends is a challenging endeavor that has been significantly advanced by the advent of machine learning techniques. This study investigates the effectiveness of various models, including Long Short-Term Memory (LSTM), XGBoost, Support Vector Machine (SVM), and hybrid models combining LSTM with XGBoost and SVM, in forecasting stock prices. Our results indicate that the LSTM model outperforms others, demonstrating superior predictive accuracy. A

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Kamal, Saurabh, Sahil Sharma, Vijay Kumar, Hammam Alshazly, Hany S. Hussein, and Thomas Martinetz. "Trading Stocks Based on Financial News Using Attention Mechanism." Mathematics 10, no. 12 (2022): 2001. http://dx.doi.org/10.3390/math10122001.

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Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and K

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Wang, Yuming, Yu Li, and Hua Zou. "Masked Face Recognition System Based on Attention Mechanism." Information 14, no. 2 (2023): 87. http://dx.doi.org/10.3390/info14020087.

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With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recogn

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Si, Nianwen, Wenlin Zhang, Dan Qu, Xiangyang Luo, Heyu Chang, and Tong Niu. "Spatial-Channel Attention-Based Class Activation Mapping for Interpreting CNN-Based Image Classification Models." Security and Communication Networks 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/6682293.

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Convolutional neural network (CNN) has been applied widely in various fields. However, it is always hindered by the unexplainable characteristics. Users cannot know why a CNN-based model produces certain recognition results, which is a vulnerability of CNN from the security perspective. To alleviate this problem, in this study, the three existing feature visualization methods of CNN are analyzed in detail firstly, and a unified visualization framework for interpreting the recognition results of CNN is presented. Here, class activation weight (CAW) is considered as the most important factor in

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Zhang, Mengya, Yuan Zhang, and Qinghui Zhang. "Attention-Mechanism-Based Models for Unconstrained Face Recognition with Mask Occlusion." Electronics 12, no. 18 (2023): 3916. http://dx.doi.org/10.3390/electronics12183916.

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Masks cover most areas of the face, resulting in a serious loss of facial identity information; thus, how to alleviate or eliminate the negative impact of occlusion is a significant problem in the field of unconstrained face recognition. Inspired by the successful application of attention mechanisms and capsule networks in computer vision, we propose ECA-Inception-Resnet-Caps, which is a novel framework based on Inception-Resnet-v1 for learning discriminative face features in unconstrained mask-wearing conditions. Firstly, Squeeze-and-Excitation (SE) modules and Efficient Channel Attention (EC

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Xue, Mengfan, Minghao Chen, Dongliang Peng, Yunfei Guo, and Huajie Chen. "One Spatio-Temporal Sharpening Attention Mechanism for Light-Weight YOLO Models Based on Sharpening Spatial Attention." Sensors 21, no. 23 (2021): 7949. http://dx.doi.org/10.3390/s21237949.

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Attention mechanisms have demonstrated great potential in improving the performance of deep convolutional neural networks (CNNs). However, many existing methods dedicate to developing channel or spatial attention modules for CNNs with lots of parameters, and complex attention modules inevitably affect the performance of CNNs. During our experiments of embedding Convolutional Block Attention Module (CBAM) in light-weight model YOLOv5s, CBAM does influence the speed and increase model complexity while reduce the average precision, but Squeeze-and-Excitation (SE) has a positive impact in the mode

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Amin, Rashid. "Urdu Sentiment Analysis Using Deep Attention-based Technique." Foundation University Journal of Engineering and Applied Sciences <br><i style="color:yellow;">(HEC Recognized Y Category , ISSN 2706-7351)</i> 3, no. 1 (2022): 7. http://dx.doi.org/10.33897/fujeas.v3i1.564.

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Sentiment analysis (SA) is a process that aims to classify text into positive, negative, or neutral categories. It has recently gained the research community's attention because of the abundance of opinion data to be processed for better understanding and decision-making. Deep learning techniques have recently shown tremendous performance, with a high tendency to reveal the underlying semantic meaning of text inputs. Since deep learning techniques are seen as black boxes, their effectiveness comes in the form of interpretability. The major goal of this article is to create an Urdu SA model tha

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Zhou, Lixin, Zhenyu Zhang, Laijun Zhao, and Pingle Yang. "Attention-based BiLSTM models for personality recognition from user-generated content." Information Sciences 596 (June 2022): 460–71. http://dx.doi.org/10.1016/j.ins.2022.03.038.

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Zhang, Lin, Huapeng Qin, Junqi Mao, Xiaoyan Cao, and Guangtao Fu. "High temporal resolution urban flood prediction using attention-based LSTM models." Journal of Hydrology 620 (May 2023): 129499. http://dx.doi.org/10.1016/j.jhydrol.2023.129499.

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Wang, Zhenyi, Pengfei Yang, Linwei Hu, et al. "SLAPP: Subgraph-level attention-based performance prediction for deep learning models." Neural Networks 170 (February 2024): 285–97. http://dx.doi.org/10.1016/j.neunet.2023.11.043.

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Hao, Cuiping, and Ting Yang. "Deep Collaborative Online Learning Resource Recommendation Based on Attention Mechanism." Scientific Programming 2022 (March 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/3199134.

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In view of the lack of hierarchical and systematic resource recommendation caused by rich online learning resources and many learning platforms, an attention-based ADCF online learning resource recommendation model is proposed by introducing the attention mechanism into a deep collaborative DCF model. Experimental results show that the proposed ADCF model enables an accurate recommendation of online learning resources, reaching 0.626 and 0.339 on the HR and NDCG metrics, respectively, compared to the DCF models before improved, up by 1.31% and 1.25%, and the proposed ADCF models by 1.79%, 2.17

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