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Sea Surface Temperature Prediction Based on Spatio-Temporal Graph Contrastive Learning Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14228-14239页

作者： Dai, Weihan He, Xiaoyu Geng, Xiulin Zhang, Shuai Gao, Ziheng Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou310018 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

There is a growing interest in using spatio-temporal graph networks to predict sea surface temperature (SST). Nevertheless, numerous existing methodologies struggle to accurately capture both local and global spatial dependencies and are red frequently vulnerable to data noise. To tackle these challenges, we introduce a novel framework known as Spatio-Temporal Graph Contrastive Learning Network (STGLN), which enhances the precision of sea surface temperature forecasts. Notably, STGLN employs both feature-level and topology-level data augmentation techniques to alleviate the influence of data noise. Subsequently, the refined SST feature matrix and adjacency matrix are analyzed through a dual-branch network with shared parameters, specifically designed to effectively capture both local and global spatial dependencies within the SST time series data. Furthermore, we propose an auxiliary task for spatio temporal contrastive learning, which aims to mitigate the sparsity of supervised signals and to efficiently extract essential spatio temporal information. Comprehensive experiments conducted on real-world SST datasets demonstrate that STGLN significantly surpasses other advanced methodologies. © 2008-2012 IEEE.

关键词： Contrastive Learning

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Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

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Chinese Journal of Electronics 2025年第2期34卷 698-711页

作者： Xie Wenwu Xiong Ming Ren Ziqing Wang Ji Yang Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang 414006 China School of Computer Science Hefei Normal University Hefei 230001 China College of Physical Science and Technology Central China Normal University Wuhan 430079 China

With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight \begin{document}$\alpha$\end{document} term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight \begin{document}$\alpha$\end{document} term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.

关键词： Semantic communication Residual neural networks (ResNets) Joint control mechanisms Edge reasoning

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Automated Tumor Detection using Convolution Neural Network 5

Automated Tumor Detection using Convolution Neural Network

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Raturi, Ayush Joshi, Kireet Jain, Paras Gupta, Vishan Kumar Gupta, Avdhesh Meena, Jaishree Graphic Era Deemed to be University School of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vit Bhopal University School of Computing Engineering and Artificial Intelligence MP Bhopal India Amity University Punjab Amity School of Engineering & Technology Punjab Mohali India Akg Engineering College Department of Cse UP Ghaziabad India Amity School of Biological Sciences Amity School Punjab Mohali India

ISBN: (纸本)9798350376685

Brain tumors are quite formidable primarily because they can occur at any place within the brain and may also exhibit dissimilar behavior patterning. The disease in its initial stage is not very dangerous, if it is not treated then the result is a lot of damage to the brain. To overcome these challenges, further research and development is needed and more specifically the implementation of new technologies. This paper seeks to contribute to these goals by developing a deep learning convolutional neural network with transfer learning approach, that can help in detection of brain tumors with high accuracy and efficiency. In the long run, it should help in the early diagnosis and timely appropriate treatment of patients which should somehow contribute to the development of neurooncology. This study proposes a model that involves MRI for detection of brain tumors through the application of deep learning. The high performance of this model is attributed to the transfer learning technique that has been applied on VGG16 trained on 3000 medical MRI images of the human brain. The proposed model provided quite satisfactory accuracy of 99%. 33% on this dataset. Using this automated detection model, it is possible for the healthcare professionals to be able to predict the presence of brain tumors quite well using this system. © 2024 IEEE.

关键词： Convolutional neural networks

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A review of explainable AI in medical imaging: implications and applications

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International Journal of computers and Applications 2024年第11期46卷 983-997页

作者： Kinger, Shakti Kulkarni, Vrushali Department of Computer Engineering and Technology WPU School of Computer Science & Engineering Dr. Vishwanath Karad MIT World Peace University Maharashtra India

Deep learning approaches have attained remarkable success across various artificial intelligence applications, spanning healthcare, finance, and autonomous vehicles, profoundly impacting human existence. However, their black-box nature, lack of transparency, and inability to elucidate conclusions have hindered their adoption in high-risk applications. Explainable Artificial Intelligence (XAI) encompasses a suite of tools, approaches, and algorithms aimed at furnishing highly accurate explanations while preserving robust accuracy. This research paper investigates the intricate and evolving domain of Explainable Artificial Intelligence (XAI), particularly its implications in healthcare, with the objective of fostering trust in AI utilization within this domain. It encompasses a spectrum of topics, including a compendium of tasks XAI should fulfil in medical imaging, an examination of current methodologies for yielding transparent and understandable outcomes in medical imaging, criteria for assessing AI system explainability, and recommendations for integrating XAI into medical imaging practices. This review facilitates the selection of suitable and efficient XAI techniques for medical imaging while aiding developers in grasping the fundamentals of these methodologies. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Medical imaging

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Smart Healthcare in Improving the Quality of Life: Revolutionizing with Application of IoT and Block Chain technology

Smart Healthcare in Improving the Quality of Life: Revolutio...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Das, Lipsa Singh, Divya Vats, Satvik Taliyan, Khushi Bhargava, Deepshikha Bhatnagar, Devang Amity School Of Engineering & Technology Amity University UP Greater Noida India Graphic Era Hill University Graphic Era Deemed To Be University Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

One of the many essential elements of life is one's health. Our healthcare systems has to possess to stay as secure as well as successful as it can be hoping for a healthier society. The establishment of sophisticated medical facilities which improve tracking of patients evaluation, and customized therapy has been brought feasible by the Internet Of Things(IoT), which is having an enormous impact upon the healthcare sector. In addition, healthcare facilities, pharmaceutical companies, diagnostic centres, and doctors might freely trade and maintain information and data about patients effectively with the use of blockchain-based technology. Blockchain-based tools are adequate to build confidence and recognized and access incorrect information provided by doctors. Blockchain innovations may therefor help with the privacy, efficiency, safety, and visibility of health related information trade in the medical sector. Through the help of such technology, hospitals can get precise as superior knowledge that will improve client analysis of data and help with the efficient handling of illness. Substantial studies and evaluations has been done to look into every potential as the use of blockchain technology possess to improve the current situation of the healthcare in general. The study seeks to provide an overview of the current state and advancement in the industries which promotes IoT- based healthcare systems. As a result, the study additionally explores into the possibilities of merging healthcare sensors, wearable gadgets, and various other modern medical tools that enable immediate time gathering of information. The article additionally dicusses the dangers and weakness that exist in that plag in IoT-based healthcare systems as well as its features, and also in addition to the results and remedies which currently exist to fix such issues and problems while safeguarding private information and data about patients. Lastly, this presents as well as examines and array for a

关键词： Diagnosis

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Merging the Strengths of GPT-4 and DeepLab for Semantic Text Understanding in Digital Textbooks

Merging the Strengths of GPT-4 and DeepLab for Semantic Text...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Anusha, B. Borah, Asha Rani Mishra, Gaurav Thethi, H Pal Al-Allak, Mustafa Abdulhussein Vidhya, B. Veda Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Computer Science Engineering Bangalore India Amity School Of Engineering And Technology Amity University Greater Noida India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Najaf Iraq Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

Gathering all the necessary data is the major objective of this study so that a comprehensive picture can be painted of how new technologies might improve students' understanding of semantic content in digital textbooks. The recommendations for this approach make use of TIFA, CEG, and ACA, or the Adaptive Complexity Adjustment. This approach combines the best features of various algorithms. TIFA makes these learning aids easy to completely comprehend by combining written and visual material. Answering students' queries and providing answers that make sense in the context is CEG's responsibility. To make learning more tailored to everyone, ACA changes the difficulty of the content depending on performance metrics. Conversely, when students ask CEG questions, it finds answers. We compare the proposed method, Semantix Blend, to two more popular approaches, Textbook Classic and Trad Edu. Interpreting text, evaluating visual information, ensuring consistency in context, enabling customisation, making content more accessible, enhancing learning results, and many more advantages are all possible with Semantix Blend. The study's findings will contribute to the improvement of digital textbooks, leading to improved instruction for students in the long term. © 2024 IEEE.

关键词： Semantics

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Double quantum images encryption scheme based on chaotic system

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Chinese Physics B 2024年第4期33卷 305-320页

作者：蒋社想李杨石锦张茹 School of Computer Science and Engineering Anhui University of Science and TechnologyHuainan 232001China Anhui Key Laboratory of Mine Intelligent Equipment and Technology Anhui University of Science&TechnologyHuainan 232001China

This paper explores a double quantum images representation(DNEQR)model that allows for simultaneous storage of two digital images in a quantum superposition ***,a new type of two-dimensional hyperchaotic system based on sine and logistic maps is investigated,offering a wider parameter space and better chaotic behavior compared to the sine and logistic *** on the DNEQR model and the hyperchaotic system,a double quantum images encryption algorithm is ***,two classical plaintext images are transformed into quantum states using the DNEQR ***,the proposed hyperchaotic system is employed to iteratively generate pseudo-random *** chaotic sequences are utilized to perform pixel value and position operations on the quantum image,resulting in changes to both pixel values and ***,the ciphertext image can be obtained by qubit-level diffusion using two XOR operations between the position-permutated image and the pseudo-random *** corresponding quantum circuits are also *** results demonstrate that the proposed scheme ensures the security of the images during transmission,improves the encryption efficiency,and enhances anti-interference and anti-attack capabilities.

关键词： double quantum images encryption chaotic system pixel scrambling XOR operation

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Visual Prompting Unsupervised Domain Adaptation for Medical Image Segmentation 22

Visual Prompting Unsupervised Domain Adaptation for Medical ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lu, Ziru Zhang, Yizhe Zhou, Yi Chen, Geng Zhou, Tao School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Engineering Southeast University China School of Computer Science and Engineering Northwestern Polytechnical University China

ISBN: (纸本)9798331520526

Unsupervised domain adaptation (UDA), aimed at improving the segmentation performance of deep models on un-labeled data, has attracted considerable attention. Recently, the Segment Anything Model (SAM) has gained widespread attention in various scenarios. In this work, we propose a Visual Prompting UDA (VP-UDA) framework for medical image segmentation, which leverages SAM's ability to improve overall generalization performance. Specifically, we first present a Hybrid Prompting Strategy (HPS) to empower SAM with profound downstream task-specific knowledge. Moreover, a SAM-based Guidance Learning (SGL) scheme is proposed to enhance the learning process of the segmentation model. Then, we propose a Consistency-based Pseudo-label Selection (CPS) strategy to discern and exclude the outlier points in the target feature space. Furthermore, a Frequency Prior-induced Fusion (FPF) module is proposed to effectively integrate the results from SAM and the segmentation model. Experimental results show the effectiveness and superiority of our model over other state-of-the-art UDA methods. © 2025 IEEE.

关键词： Semantic Segmentation

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Design and Architectural Implementation of Consortium Blockchain Based Framework for Open Banking Customer Consent and Data Handling

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SN computer science 2024年第2期5卷 271页

作者： Ghosh, Abir Mukhopadhyay, Indraneel Chakraborty, Subhalaxmi University of Engineering and Management West Bengal Kolkata India Amity School of Information Technology Amity University West Bengal Kolkata India Department of Computer Science University of Engineering and Management West Bengal Kolkata India

Presently blockchain-based open banking approaches have gained immense popularity in financial sector. In open banking, customer consent is mandatory by regulation before any customer data can be shared by bank with Third Party Providers (TPP) following strict security protocols. Recent study shows that customer remains unaware about private data leakages where data is shared by bank not adhering agreement between bank and customer. To mitigate such type of problem, an innovative consortium permissioned blockchain-based structure comprising banks, regulators, Third Party Providers, and customers is addressed in this proposed work. The implemented framework manages consent handling and data sharing under the gamut of blockchain security. Security analysis of the framework proves it to be secure from threats and performance evaluation of the framework is satisfactory with more than fifty customer data parameters and ten nodes of blockchain. This blockchain based framework traces data sharing non-compliances and reports it real-time basis to customer. Thus it enhances the transparency in the whole open banking data sharing process and boosts customer confidence to adopt open banking practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bank Blockchain Consent management Customer trust Data violation Open banking Regulator Third party providers

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2025年第16期84卷 16249-16280页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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