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Emerging Research in Computational science (ICERCS), International Conference on

作者： Arun Sekar Rajasekaran Haribabu J Ramnjaneyulu GV Sivakumar T Mohanarathinam A Velmurugan T Department of Electronics and Communication Engineering SR University Telangana India Department of AIMLDS QIS College of Engineering and Technology Andhra Pradesh India Department of Civil Engineering QIS College of Engineering and Technology Andhra Pradesh India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Tirupur India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In this day and age of digital technology, the verification of documents plays an essential part in a variety of spheres, including the validation of identities, legal contracts, academic credentials, and financial transactions. Traditional document verification methods often face security issues, data tampering, and fraud. This research proposes a Blockchain-Based Document Verification Scheme (BBDVS) to improve document verification security and fraud management. This addresses concerns. The BBDVS uses blockchain technology's immutability and decentralization to create a safe, public document verification platform. Cryptography ensures the integrity, validity, and non-repudiation of checked papers. Smart contracts, blockchain-based self-executing agreements, automate and expedite verification. A prototype system is built and tested to determine BBDVS efficacy. The system simulates various document verification scenarios. Compared to conventional approaches, this technology is more secure, efficient, and reliable. It also shows how the scheme may avoid fraud, decrease costs, and simplify document verification for various businesses. The research found that the BBDVS may significantly improve fraud control and document verification security. This strategy could revolutionize document verification in banking, healthcare, education, and government services. More research is needed to solve the scalability, privacy, and regulatory issues that arise when implementing blockchain-based systems on a large scale.

关键词： Industries Scientific computing Scalability Smart contracts Education Banking Fraud Blockchains Security Reliability

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

arXiv

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Consumer Behavior and Satisfaction Rate in Social Media Purchasing Ads Using Radial Basis Function Networks (RBFNS) Based BAT Algorithm 3

Consumer Behavior and Satisfaction Rate in Social Media Purc...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Lilly Florence, M. Varun, T. Senthilkumar, S. Florence, N. Sheeba Seal, Sudeshna Sangeetha, S. Adhiyamaan College of Engineering Tamlinadu India Anna University Department of Management Studies TamilNadu Chennai India Kalasalingam Academy of Research and Education Tamilnadu India Sona College of Technology Department of Mathematics Tamil Nadu Salem India OmDayal Group of Institutions West Bengal Howrah India Velammal College of Engineering and Technology Depatment of Computer Science and Engineering TamilNadu Madurai India

ISBN: (纸本)9798331542375

Companies who wish to enhance their marketing plans must first be quite conscious of consumer behaviour and satisfaction with social media campaigns allowing purchase capability. Many unstructured data generated by social media channels makes it challenging to find feasible application concepts. Usually, the conventional approaches do not sufficiently show the non-linear interactions between consumer reactions and advertisement effectiveness. While current machine learning methods such artificial neural networks (ANN) or Support Vector machines (SVM) can offer insights, they lack the optimising force required to properly fine-tune parameters. The forecasts of satisfaction rates thus differ from ideal. Better predictive accuracy is obtained in the technique by using a Bat Algorithm in concert with Radial Basis Function Networks (RBFNs). The Bat Algorithm maximises RBFN parameters, which include weights, spread, and centers, so improving their performance. Ten thousand consumer records from social media buying campaigns made up the data used for the study. Among the data were click-through rates and degrees of satisfaction. For model validation as well as training, an 80-20 split was used. Predicting consumer satisfaction with a 96.2% accuracy, the RBFN-Bat model outperformed already in use approaches including artificial neural networks (92.5%) and support vector machines (89.7%). Moreover, the mean absolute error (MAE) dropped to 0.045 and the computational efficiency rose by 18% in comparison to earlier applied techniques. © 2025 IEEE.

关键词： Consumer behavior

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E-Governence Services using Artificial Intelligence Techniques

E-Governence Services using Artificial Intelligence Techniqu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： P. Chinnasamy D. Tejaswini S. Dhanasekaran K. Ramprathap K Lakshmi Priya Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology Kalasalingam Academy of Research and Education(Deemed to be University) Srivilliputtur India Department of Management Studies M. Kumarasamy College of Engineering Karur India Department of ECE Peri institute of technology Chennai

In a wide variety of fields, AI technology (AI) has lately evolved to state-of-the-art levels. It still has a lot of obstacles to overcome before it can be ready for e-government applications, including setting up the systems and interacting with residents online. In this research, we tackle the problems with e-government systems & suggest a framework that makes use of AI technology to improve and streamline e-government operations. We first design a model for the administration of e-government information resources in particular. Second, we frequently create a variety of deep-learning algorithms with the intention of transforming numerous e-government services. Third, we tend to suggest an intelligent e-government platform architecture that facilitates the emergence and execution of AI e-government applications. Our overriding objective aims to advance this level of e-government services by using reliable AI approaches in order to shorten processing times, reduce costs, and raise public satisfaction.

关键词： Deep learning Information resources Costs computer architecture Reliability Artificial intelligence Informatics

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Non-Intrusive Load Monitoring Techniques for Intelligent Energy Management: A Comparative Study of FHMM and LSTM Approach

Non-Intrusive Load Monitoring Techniques for Intelligent Ene...

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

作者： N. Rajkumar Anitha Govindaram R. Geetha Subhamathi A S F Nithya S. Jose Anand A. Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu Department of EEE Karpagam Academy of Higher Education Eachanari Coimbatore Tamilnadu Department of IT Veltech High-tech Dr Rangarajan Dr.Sakunthala Engineering College Avadi Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This study addresses the problem of detecting and segregating electrical loads using two alternative data mining algorithms for Non-Intrusive Load Monitoring (NILM). The initial method makes use of a Factorial Hidden Markov Model (FHMM), which uses hidden state observations to forecast each device's usage. The second method uses a Long Short-Term Memory (LSTM) network and time-windowed data to measure each device's consumption over a given period. Experimental results, measured using the Root Mean Square Error (RMSE) metric, reveal that both techniques successfully address the disaggregation challenge, with low error rates and exact forecasting of individual energy use. The study emphasises the importance of input sequences obtained from mixed signals on model predictions and comparison results. Furthermore, using publically available tools and datasets improves data pretreatment efficiency and facilitates the use of NILM approaches. This study improves NILM methodologies by providing insights into effective modelling strategies and emphasising the importance of readily available resources in the energy disaggregation field. Its purpose is to encourage more research and improvement in this field.

关键词： Performance evaluation Energy consumption Load monitoring Visual analytics Hidden Markov models Energy measurement Predictive models Data mining Root mean square Long short term memory

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More task-balanced Class-Incremental Learning

More task-balanced Class-Incremental Learning

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Junzhi Chen Tian Wang Deyuan Liu Jian Wang Yao Fu Hichem Soussi School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence SKLSDE Beihang University Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

Although deep learning has achieved high performance on many tasks, neural networks suffer from catastrophic forgetting in incremental learning scenarios. Class-Incremental Learning focuses on the scenario where new category data continuously arrives. PASS (Prototype Augmentation and Self-Supervised) is a classic method that does not require storing old exemplars. Based on PASS, we propose three improvement measures from the perspective of making the new and old tasks more balanced. Firstly, we propose a balanced prototype replay (Balanced PR) strategy to make the image number of new and old categories that the model head encounters more balanced. Secondly, at the feature knowledge distillation (FKD) stage, we assign different weights to the incoming data based on the confidence of the old model, which is named Focal FKD. The goal is to minimize interference with learning new task data while preserving the representation capability of the model backbone on old data. Thirdly, we combine FKD with weight regularization on the model backbone. In the feature space, FKD is beneficial for preserving the positions of the old categories while weight regularization helps to separate the new and old categories well. Experiments on the benchmark indicate that there has been obvious improvement in the performance of PASS after our modifications.

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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An Approach to Ensemble Simplified ResNet Models-Using Egyptian Hieroglyphs Data Set

An Approach to Ensemble Simplified ResNet Models-Using Egypt...

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Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Zixiang Yan Kecheng Jin Jingkun Hou Pu Zhang College of Computer and Information Engineering (College of Artificial Intelligence) Nanjing Tech University Nanjing China Nanjing Hankai Academy Nanjing China School of Inteligence Science and Technology University of Science and Technology Beijing Beijing China Austin Friars St Monica's School Etterby Scaur Carlisle Cumbria UK

Since the inception of computer vision, recognizing text, including both printed and handwritten characters, has posed a significant challenge for traditional machine learning models. However, only a handful of these models have focused on ideographical scripts. Similarly, while mathematical solutions to common neural network problems such as degradation and computational complexity have been studied, possible shortcuts are often overlooked. This paper presents an innovative approach by introducing a novel ensemble network called "SparrowNet" and compares it to ResNet18 and GoogLeNet V2. Using an open-source Egyptian hieroglyph dataset, the paper focuses on evaluating the efficiency and accuracy of different networks using various performance metrics. Additionally, the paper discusses the advantages of the two existing models and explains the reasons behind the modifications made to SparrowNet. Through this research, it has been demonstrated that the use of 1x1 convolutions can significantly improve speed and reduce computational complexity, while a voting mechanism can be a novel solution to prevent degradation, and the modified network is capable to solve this particular character recognition task.

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European Control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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Radio Diversity for Reliable Communication in WSNS

Radio Diversity for Reliable Communication in WSNS

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Ruchika Tondon Shivangi Ghaldiyal Neeraj Kaushik V. Mathan Kumar S. Padmanaban Gajanan H Chavhan Department of Petroleum Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Applications Invertis University Bareilly Uttar Pradesh Department of ECE TMU Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Karpagam Institute of Technology Coimbatore E&TC Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The difficulties are often unbearable when it comes to wireless sensor deployment in real-world scenarios. The unpredictable nature of physical settings has a significant impact on radio connection dependability, leading to intermittent connectivity and frequent interruptions. Conventional deterministic methods for handling unstable connections, including increasing transmission power or network density, are insufficient to solve this problem. We propose a unique architecture for a dual radio network designed for modern wireless sensing networks. Data travels over a number of connections in mesh networks to go from source node to destination node. A persistent difficulty is the fluctuation of radio connection quality, which is driven by changes in the environment and radio interference. Modern data gathering methods are designed to quickly adjust to changes in network connection and correct routing conditions. However, connection losses become unavoidable when there aren't many other paths available inside the network. Even with other paths, redirected traffic plus more control traffic may cause packet collisions and network congestion, which can result in less than ideal performance and responsiveness. Moreover, the cost of fixing link failures increases with network size since the routing state requires propagation throughout the whole network. In order to build large, trustworthy, and effective WSNs that cover areas with diverse and variable environmental conditions, this article focuses on improving the dependability of main radio connections. However, there are only so many ways to enhance connection quality, and choosing node sites strategically to create a robust wireless mesh is still a difficult task.

关键词： Wireless communication IEEE 802.15 Standard Fading channels Wireless sensor networks Costs Scattering Routing

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