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Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing Images 22

Swin Transformer with Multi-Scale Residual Attention for Sem...

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Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Yuanyang Lin Da-han Wang Yun Wu Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method.

关键词： Dilated Convolution Semantic Segmentation Remote Sensing Swin Transformer

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A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules 22

A Lightweight Brain Tumor Segmentation Network Based on 3D I...

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Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Yuchao Liu Xia Du Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance.

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Exploring Content-Based and Meta-Data Analysis for Detecting Fake News Infodemic: A case study on COVID-19

Exploring Content-Based and Meta-Data Analysis for Detecting...

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pattern recognition Systems (ICPRS), international conference on

作者： Oluwaseun Ajao Ashish Garg Má rjory Da Costa-Abreu Department of Computing Sheffield Hallam University

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466950

The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was spreading, infecting people and claiming thousands of lives so was the spread and propagation of fake news, misinformation and disinformation about the event. These included the spread of unconfirmed health advice and remedies on social media. In this paper, false information about the pandemic is identified using a content-based approach and metadata curated from messages posted to online social networks. A content-based approach combined with metadata as well as an initial feature analysis is used and then several supervised learning models are tested for identifying and predicting misleading posts. Our approach shows up to 93 % accuracy in the detection of fake news related posts about the COVID-19 pandemic.

关键词： COVID-19 Pandemics Social networking (online) Supervised learning Metadata Predictive models pattern recognition

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Integrating Reliable AI to Boost Blockchain's Transparency and Accountability

Integrating Reliable AI to Boost Blockchain's Transparency a...

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Advanced computing and Emerging Technologies (ACET), international conference on

作者： G B Renuka Pramod Kumar Patjoshi Upendra Aswal G. Manikandan L.N. Jayanthi Ashish Kaushal Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andra Pradesh India School of Management Centurion University of Technology and Management Ramachandrapur Odisha India Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Commerce General Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Jindal Global Business School O P Jindal Global University Sonipat Haryana India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The power of blockchain technology to provide decentralized networks accountability and transparency has transformed several industries. However, there are limitations to traditional blockchain systems, like data tampering and insufficient real-time validation capabilities. The study proposes a ground-breaking approach to solving these issues by integrating AI algorithms to enhance accountability and transparency in blockchain ecosystems. The proposed system improves consensus processes, identifies abnormalities, and validates transactions in real-time by using AI-driven methods. Using rigorous data gathering, pre-processing, model selection, and continuous review, the system outperforms existing systems in terms of fraud detection accuracy, false positives and negatives, and transaction validation performance. Through a significant increase in security, scalability, and efficiency, the results indicate the potential of AI-driven blockchain solutions to support trust and reliability in transaction processing and fraud detection. The proposed system outperforms existing systems with a 95% fraud detection rate, a 5% false positive rate, and a 5% false negative rate. Furthermore, the scalability and efficacy of the proposed AI-enhanced blockchain system are demonstrated by the significant improvements in transaction validation performance across a variety of transaction volumes.

关键词： Training Adaptation models Reviews Scalability Computational modeling Real-time systems Blockchains Fraud pattern recognition Reliability

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Adaptive ternary pattern based on supervised learning approach for ground-based cloud type classification

Adaptive ternary pattern based on supervised learning approa...

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international conference on Image Processing and Capsule Networks, ICIPCN 2020

作者： Hoang, Vinh Truong Ho Chi Minh City Open University Ho Chi Minh City Viet Nam

ISBN: (纸本)9783030518585

Ground-based cloud images recognition is a difficult task because of extreme varieties in cloud appearance under various atmospheric conditions. Local Binary pattern and its variants are widely applied due to its efficiency for texture classification. Among them, Local Ternary pattern (LTP) descriptor is the most common to characterize cloud images. In this approach, an additional parameter is used to define a tolerance for measuring the similarity between different gray intensities. This paper proposes a new approach to determine a suitable value of the parameter for LTP descriptor by the supervised learning approach. The proposed method is evaluated on the SWIMCAT database. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Image classification

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Memory-Guided Contrastive and Triplet Separation for Weakly-Supervised Disease Detection

Memory-Guided Contrastive and Triplet Separation for Weakly-...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinwen She Qiong Li Andy J. Ma School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Cancer Center Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Weakly-supervised disease detection has the great potential to alleviate the time-consuming and labor-intensive burden of manual annotations in instance level. While existing methods extract normality prototypes encoding normal patterns to improve the detection performance, they may fail to detect subtle anomalies without considering the diverse abnormal patterns. In this paper, we propose to recognize both normal and abnormal patterns for feature representation learning in disease detection based on a dual memory network. To learn discriminative memory banks and classifiers, dual memory loss is incorporated with a feature magnitude separation loss for model training. With the proposed contrastive feature separation loss and triplet feature separation loss, feature discriminability is further improved to obtain a more separable decision boundary. Additionally, we collect a large-scale CT dataset to evaluate lung tumor detection. Extensive experiments on the publicly available PANDA-MIL dataset and the collected LUNG-MIL dataset demonstrate the superiority of our proposed method compared to the state-of-the-art approaches for weakly-supervised disease detection.

关键词： Training Representation learning Lung cancer Prototypes Manuals Feature extraction Encoding pattern recognition Diseases Tumors

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Fair Formation Control of Multiple Agents Using Ant Colony Optimization

Fair Formation Control of Multiple Agents Using Ant Colony O...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Yoshie Suzuki Stephen Raharja Toshiharu Sugawara Computer Science and Communications Engineering Waseda University Tokyo Japan

This paper proposes a method to automatically generate consecutive formation transition paths of multiple agents, such as in unmanned aerial vehicle (UAV) shows, considering the reduced total travel distance as well as fair travel distances of individual agents. A UAV show is a type of entertainment where various artistic formation patterns are presented in the air by UAV agents, such as drones. Although several studies discussed the formation transition for UAV shows by considering the minimization of the total travel distance, few of them considered the fairness in moving distances among agents and transitions for long distances, which result in battery runout and shorter performance times Moreover, as a UAV show is performed by several agents, their battery capacities are usually not large; however, the performance time is affected by battery consumption. Our proposed method, which is based on ant colony optimization (ACO), enables agents to present longer performances in 3D space by considering the fairness in travel distance. Experiments showed that agents could perform approximately 20% more formation patterns without collisions with our proposed method based on ACO than those with the conventional method that is also based on ACO.

关键词： pattern formation Ant colony optimization Three-dimensional displays Surveillance Robot kinematics Scattering Aerospace electronics

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Enhancing User Authentication for Financial Application Using Multi-Modal Behavioural Biometrics - A Survey

Enhancing User Authentication for Financial Application Usin...

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Data, Computation and Communication (ICDCC), international conference on

作者： Ajay Sriram Premanand V Navin Kumar L Jayanth Kumar S School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

It is essential to ensure secure and easy user authentication in the domain of financial applications. Conventional techniques frequently fall short of offering strong security without sacrificing user pleasure. This research integrates fingerprint, signature, and eye movement patterns to provide a sophisticated multi-modal behavioral biometric identification system. In current systems, all three biometric modalities are infrequently integrated, especially for financial applications where security and usability are critical. The suggested solution promises to provide continuous authentication for mobile devices, improve authentication accuracy, and address issues such as template ageing. The multi-modal behavioral biometric is fused using Image-level fusion. It creates a single image map of fingerprint, signature, and eye movement patterns and matches it with the input image the system receives. In case of a perfect match the user is authenticated to use the service. A survey of 50 participants showed strong support for the multimodal approach, with 90% perceiving it as necessary and effective. By utilizing their combined capabilities and minimizing individual biometric weaknesses, the integrated system exhibits better security. This work also looks at the system's practical application and computing effectiveness on mobile devices with limited resources. This study offers a comprehensive solution for secure, effective, and user-friendly authentication in financial applications by filling in the gaps in the current research.

关键词： Surveys Authentication Finance Fingerprint recognition Aging Mobile handsets Filling Security Usability pattern matching

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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. The weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. Then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. The proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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Enigmas in Emotion Detection Techniques Tools: Challenges and Concerns

Enigmas in Emotion Detection Techniques Tools: Challenges an...

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international conference on Parallel Distributed and Grid computing (PDGC)

作者： Vandana Devi Avinash Sharma Computer Science & Engineering MMDU Mullana Ambala India

ISBN: (纸本)9781665454025

Emotion detection is a branch of sentiment analysis that focuses on the identification and study of emotions. Text mining, analysis of extracted information, and in-depth research are now at the centre of organisational success due to the development of web 2.0. It enables service providers to provide their clients with specialized services. Due to the simplicity of obtaining data and the enormous advantages its deliverables give, text-based extraction and analysis are now the topics of much research. The notion of ED from texts is examined in this article, which also highlights the major techniques that academics have taken to develop ED systems that employ texts. The proposals' key contributions, methods used, datasets consulted, outcomes attained, and strengths and limitations are described. Additionally, emotion-labelled data sources are offered to give beginners access to appropriate text datasets for ED. The essay concludes by outlining several unresolved problems and potential lines of inquiry for text-based ED. Due to its numerous potentials uses in business, cultural studies, cognitive science, human-computer interaction, cognitive computing, etc., emotion recognition in the text has recently gained popularity. The availability of a wealth of textual material, particularly opinionated and self-expressive writing, also considerably increased public awareness of this issue.

关键词： Text mining Human computer interaction Emotion recognition Sentiment analysis Web 2.0 soft sensors Writing

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