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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Tyagi, Pankaj Kumar Beemkumar, N. Agarwal, Ankita Kaushik, Harshita Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Stomach surgical operation can cause damage to intestinal tissue and is associated with a better risk of adhesive intestinal ailment. The main symptom of the adhesive intestinal disorder is intestinal adhesion, a continual and probably life-threatening hassle. Glucocorticoid use at the time of abdominal surgical procedures has been related to an increase in the danger of adhesive intestinal sickness. In addition to recognizing this disorder’s threat and protecting factors, the existing examination evaluated the Glucocorticoid Toxicity Index (GTI) as a predictor of intestinal adhesion formation in patients’ present process of stomach surgical operation. A prospective discussion was conducted on 220 sufferers who underwent stomach surgical treatment at an urban coaching hospital. The GTI change was measured at the time of the surgical treatment, and the patients were accompanied for one year after their surgery. The consequences of the look showed that there had been a statistically significant affiliation between higher GTI and the occurrence of postoperative intestinal adhesion after a stomach surgical operation. Moreover, the facts confirmed that there had been an additive effect of other chance factors, together with the acute inflammatory procedure or impaired wound healing, on the hazard of adhesion formation while blended with GTI. Those results propose that GTI is a dependable predictor of intestinal adhesion formation after a belly operation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Transplantation (surgical)

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BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data Processing Fusion Model 24

BP-LSTM-ATTENTION: A Regional Precipitation Multisource Data...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhang, Xiaodan Wang, Hongyu You, Shaojie Zhao, Tong Li, Zi School of Computer Technology and Applications Qinghai University Qinghai Xining China Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai Xining China Qinghai Provincial Institute of Meteorological Sciences Qinghai Xining China

ISBN: (纸本)9798400710094

Qinghai Province shoulders the significant mission of maintaining ecological security, protecting the three-river source region, and safeguarding the ‘Chinese Water Tower’. To improve the accuracy of precipitation data is a great importance for studying regional ecological changes. The paper takes Qinghai Province as the research area and develops a multi-source precipitation data fusion model: BP-LSTM-ATTENTION, which is based on BP network, LSTM network and attention mechanism. The model uses satellite radar precipitation data and auxiliary data such as vegetation normalized difference index as feature data, and ground observation station data as label values, and through training and evaluation, improving the accuracy of data fusion. Then, a regional long-term and high spatial resolution precipitation fusion dataset with a spatial resolution of 1Km, a temporal resolution of 3 hours, and a time series from 2000 year to 2020 year, was generated by the data fusion model. Taking the fusion processing of precipitation data from 2016 to 2020 as an example, the fusion model successfully reduced the average RB of precipitation fusion data in the study area to 14.6%, the average RMSE to 0.1566, the average MAE to 43.706, and the average CC to 0.906. The experiment shows that the precipitation data fusion model studied in this paper has high data fusion processing accuracy and good precipitation data prediction performance. In terms of the model’s ability to capture precipitation events, the precipitation data fused by the model has an average POD of 0.818, an average FAR of 0.318, and an average MISS of 0.182 from 2016 to 2020, demonstrating its strong capability in monitoring precipitation events and good performance in reducing false alarms and missed reports. The BP-LSTM-ATTENTION model proposed in this study solves the problems of spatiotemporal correlation and time series in precipitation data of Qinghai Province, and has produced high-resolution, high spatiotemporal

关键词： Water towers

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Securing Net Banking Transactions: The Dynamic Duo of AI and Blockchain technology

Securing Net Banking Transactions: The Dynamic Duo of AI and...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Benjamin Singh J. F Karunambikai R. Department of Computer Application Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

This research paper presents a comprehensive approach to enriching security mechanisms for online banking and transactions with facial biometric authentication, iris recognition, and blockchain technology. Biometric traits, especially facial ones, are termed as a powerful, secure replacement to conventional password-oriented authentication systems, which are prone to invasions by hackers. This research focuses on the merits of face biometrics, which are characterized by dissimilar and constant textures that remain more or less same throughout life and function as “living password.” To authenticate the user's identity, the proposed system collects and analyzes the user's iris image and compares it with a secure database. All users acquire permission after using a login password and biometric verification with this multi-modal authentication system. This paper brings in a holistic approach to strengthening the security of online banking and transactions with facial biometric verification, iris recognition, and blockchain technology. Biometric traits, particularly that of the face, are termed a strong and safe alternative to the traditional password-based authentication systems, which are prone to hackers. The study emphasizes the merits of face biometrics, which are characterized by a different and consistent texture virtually unrecognizable throughout a person's life, acting like a “living password.” To identify the user, the proposed system takes an image of the user's iris and matches it to a secure database. Access is granted to authorized users only after multi-modal authentication with a login '2

关键词： Online banking Face recognition Scalability Authentication Passwords Biometric authentication Blockchains Security Artificial intelligence Iris recognition

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FedDGA: Federated Multitask Learning Based on Dynamic Guided Attention

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 268-280页

作者： Sun, Haoyun Zhao, Hongwei Xu, Liang Zhang, Weishan Guan, Hongqing Yang, Su China University of Petroleum Qingdao266580 China Shandong Data Open Innovative Application Laboratory Qingdao266580 China WINDAKA Technology Company Ltd. Qingdao266580 China Beijing University of Science and Technology Beijing100083 China Swansea University Department of Computer Science Faculty of Science and Engineering SwanseaSA2 8PP United Kingdom

The proliferation of privacy-sensitive data has spurred the development of federated learning (FL), which is an important technology for state-of-the-art machine learning and responsible AI. However, most existing FL methods are constrained in their applicability and generalizability due to their narrow focus on specific tasks. This article presents a novel federated multitask learning (FMTL) framework that is capable of acquiring knowledge across multiple tasks. To address the challenges posed by non-IID data and task imbalance in FMTL, this study proposes a federated fusion strategy based on dynamic guided attention (FedDGA), which adaptively fine-tunes local models for multiple tasks with personalized attention. In addition, this article designed dynamic batch weight (DBW) to balance the task losses and improve the convergence speed. Extensive experiments were conducted on various datasets, tasks, and settings, and the proposed method was compared with state-of-the-art methods such as FedAvg, FedProx, and SCAFFOLD. The results show that our method achieves significant performance gains, with up to 11.1% increase in accuracy over the baselines. © 2020 IEEE.

关键词： Scaffolds

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A Unified Fog Computing Architecture for Next-Generation Smart City Traffic and Crime Management

A Unified Fog Computing Architecture for Next-Generation Sma...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Vikas Rai J Ashwini K M Department Of Master Of Computer Application NMAM Institute of Technology Nitte (Deemed to be University) India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The rapid growth of urbanization has led to a surge in demand for smart city systems that can efficiently manage resources and ensure safety. This paper delves into the design and simulation of a Smart City System that leverages Fog Computing. Fog Computing involves distributing computation and storage resources across devices located at the network edge. The system integrates a diverse range of sensors, including surveillance cameras, VPR (Vehicle Plate Recognition) cameras, and audio sensors, strategically deployed across different city areas. These sensors monitor traffic, detect anomalies, and promptly provide real-time alerts. The Fog architecture enhances system responsiveness by processing data closer to its source, thereby reducing latency and bandwidth consumption. To model and simulate the interaction between various Fog devices, cloud resources, and edge-based applications, we employ CloudSim, a cloud simulation tool. By meticulously mapping different application modules to devices based on their capabilities, we simulate the execution of smart city applications, such as video capture, traffic detection, and audio alarm triggering. The results show the possibility of employing Fog Computing for building an efficient, scalable, and responsive system for a smart city. Fog computing has the potential to significantly impact large urban areas by processing data at the edge, thereby reducing latency and bandwidth consumption.

关键词： Smart cities Image edge detection Surveillance computer architecture Bandwidth Cameras Sensor systems Surges Intelligent sensors Edge computing

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Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality

Integration of Chemometric Analysis and Machine Learning for...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Gokul Padmanaban Princy Suganthi Bai S Department of Computer Application Hindustan Institute of Technology & Science Chennai India Department of Computer Science Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The integration of chemometric analysis and machine learning techniques to enhance the predictive classification of raisin quality, a vital task for agricultural and food processing industries. The study employs chemometrics to analyze physical and chemical characteristics, selecting critical features such as color, texture, and chemical composition to reduce data redundancy and optimize model accuracy. Various machine learning algorithms, including Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are applied and compared to predict raisin quality. By merging chemometric approaches with advanced machine learning methods, the proposed framework demonstrates improved classification accuracy over traditional techniques, showcasing its potential for real-world applications in quality control and automated sorting systems. Future efforts will aim to expand the dataset, refine model performance, and explore new feature selection techniques to further boost classification accuracy.

关键词： Support vector machines Accuracy Visual analytics Machine learning Quality control Artificial neural networks Real-time systems Data models Chemicals Sorting

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Domain Transfer Learning for Medical Image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. This era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. This approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. The most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Analyzing the Efficiency of Key Management Techniques Used in Information Security Models for Networked applications 5th

Analyzing the Efficiency of Key Management Techniques Used i...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Beemkumar, N. Singh, Vaishali Shree, Ritu Batra, Raman Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This study aims to investigate the performance of numerous key manipulation strategies utilized in modern statistics protection models. Especially, this painting examines the use of public key infrastructures (PKI), symmetric key infrastructures (SKI), and identity-based totally encryption (IBE) for securing application conversation in networked environments. By way of analyzing the strengths and weaknesses of each gadget, potential improvements are diagnosed. Moreover, a comparison among the unique fashions is done to pinpoint feasible weaknesses and endorse feasible mitigation strategies. The research results will enable the improvement of greater comfortable and green key management structures for networked programs, which can offer effective protection against malicious assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrastructures Pinpoint Protection Symmetric Weaknesses

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Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality

Integration of Chemometric Analysis and Machine Learning for...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Padmanaban, Gokul Princy Suganthi Bai, S. Hindustan Institute of Technology & Science Department of Computer Application Chennai India Hindustan Institute of Technology & Science Department of Computer Science Chennai India

ISBN: (纸本)9798331521394

关键词： Support vector machines

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GBC: Gaussian-Based Colorization and Super-Resolution for 3D Reconstruction 19

GBC: Gaussian-Based Colorization and Super-Resolution for 3D...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its applications in Industry, VRCAI 2024

作者： Liao, Jiecheng He, Shi Yuan, Yichen Zhang, Hui Department of Computer Science and Technology BNU-HKBU United International College ZhuHai China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400713484

In this paper, we introduce GBC, an advanced framework for transforming low-resolution, monochrome video sequences into high-resolution, colorized, and geometrically accurate 3D models. GBC combines Bidirectional Optical Flow Super-Resolution (BOF-SR) for temporal super-resolution with a novel colorization approach, Temporal Optical Flow-based Colorization (TOF-CO), designed to enhance frame-to-frame temporal consistency. The integration of these modules with COLMAP-based 3D Gaussian splatting further extends GBC's capability to reconstruct high-fidelity 3D scenes. Additionally, we created a custom dataset tailored to the challenges of low-light, low-quality historical footage, enabling robust evaluation alongside public datasets. This solution offers a new approach to video restoration, advancing temporal coherence, color accuracy, and 3D scene fidelity. The source code is publicly available at https://***/ffftuanxxx/GBC. © 2024 ACM.

关键词： 3D reconstruction

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