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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Rajesh, P.K. Shreyanth, S. Sarveshwaran, R. Nithin Chary, V. MTech in Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani India

ISBN: (纸本)9783031511660

In the financial sector, credit card fraud is a common issue that costs both people and organizations a lot of money. The ability of machine learning algorithms to automatically identify patterns and anomalies from big datasets has made them a common method for fraud detection. In order to increase the detection of credit card fraud, we suggest an improved Bayesian random forest classifier in this article. By using Bayesian optimization to choose the ideal hyperparameters for the model, we handle the difficulties and restrictions of conventional random forest classifiers. Using an openly accessible dataset on credit card fraud, the proposed method is assessed and contrasted with other cutting-edge approaches. By obtaining an accuracy of 99.6% and an area under the curve (AUC) of 0.99, our findings demonstrate that the suggested optimized Bayesian random forest classifier outperforms conventional random forest and other benchmark methods. We also examine the significance of features in fraud identification in order to show the model’s interpretability. Finally, we discuss the suggested strategy’s drawbacks and potential future developments. Our study aids in the creation of reliable and effective fraud detection tools for the finance sector. The suggested approach can be used as a decision support tool for fraud analysts and detectives and can be applied to a variety of fraud detection issues beyond credit card fraud. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data mining

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Enhancing Drug Abuse Face Recognition: A Study on Image Corruption and Restoration 18

Enhancing Drug Abuse Face Recognition: A Study on Image Corr...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Dhake, Hruturaj Agarwal, Akshay Indian Institute of Science Education and Research Department of Data Science and Engineering Bhopal India

ISBN: (纸本)9798350364132

Drug abuse poses a pressing societal issue and limits the success of face recognition algorithms. Further, when the faces are acquired in an unconstrained environment, they get affected by several environmental factors including snow, noise, and blur. One prominent limitation of the existing research is that no work has been done to mitigate the impact of drug abuse on deep face recognition. Further, the problem became complicated in the presence of input corruption. Through this study, we aim to fill the gap by investigating the effects of drug-induced facial alterations coupled with input corruptions on deep face recognition. Our study indicates that drug alteration drastically alters facial features and on top of that the corruption further degrades the image quality and hence leads to poor performance by deep face recognition networks. Therefore, the one question we asked inspired by the success of generative networks is whether these models can help mitigate the impact of drug abuse and image corruption to boost performance. Through experiments simulating real-world scenarios, we demonstrate several interesting observations about the success of generative models including diffusion and generative architectures. The efficacy of these models is not only observed for face recognition but also for the improvement of gender classification performance. To the best of our knowledge mitigation of illicit drug abuse is not explored in the literature and hence, this research contributes to enhancing the reliability of face recognition systems amidst the challenges posed by drug abuse, thus advancing both understanding and application in practical settings. © 2024 IEEE.

关键词： Generative adversarial networks

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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Simulation model optimization for bonnet polishing considering consistent contact area response

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Frontiers of Mechanical engineering 2024年第4期19卷 87-105页

作者： Yanjun HAN Haiyang ZHANG Menghuan YU Jinzhou YANG Linmao QIAN School of Mechanical Engineering Southwest Jiaotong UniversityChengdu 610031China Institute of Data Science Maastricht UniversityMaastricht 6229ENthe Netherlands

Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes,but model complexity often poses challenges to the practical implementation and efficiency of these *** paper presents an innovative method for optimizing simulation model parameters,focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model ***,controllable and uncontrollable factors in bonnet simulations are analyzed,and then a simplified contact model is developed and applied under constant force *** characterize the bonnet's contact performance,a contact area response curve is introduced,which can be obtained through a series of single spot contact ***,a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and *** average deviation of the contact area under different conditions was reduced from 22.78%before optimization to 3.43%after optimization,preliminarily proving the effectiveness of the proposed simulation optimization ***,orthogonal experiments are further conducted to validate the proposed *** comparison between the experimental and predicted material removal functions reveals a high consistency,validating the accuracy and effectiveness of the proposed optimization method based on consistent contact *** research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.

关键词： bonnet polishing simulation contact area tool influence function optimization

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A Study of data Leakage Prevention Techniques in Remote Collaborative Work Application 24

A Study of Data Leakage Prevention Techniques in Remote Coll...

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9th International Conference on Cyber Security and Information engineering, ICCSIE 2024

作者： Xu, Wenfang Zhao, Shusheng School of Data Science and Computer Engineering Xiamen Institute of Technology China

ISBN: (纸本)9798400718137

Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protection measures have increasingly become the focus of attention, posing a severe challenge to enterprise data security. To address this challenge, this paper proposes an innovative comprehensive data leakage prevention technical solution. This solution integrates cloud services and kernel technology and creates a set of data security protection systems for the remote collaboration working environment. This technical solution adopts a series of effective technical means, including asymmetric encryption, one-way data flow control, dynamic digital watermarking technology, injection attack defense, and program anti-hanging mechanism, significantly enhancing the security of data in the collaborative office system. The constructed framework includes multiple modules such as user management, service support, kernel driver, and user terminal. This technical solution can be integrated with enterprise remote collaboration office software, providing a higher level of security guarantee for the remote office environment, effectively curbing the risk of sensitive data leakage, and also providing solid technical support for the healthy and stable development of remote collaboration work. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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Accurate machine learning force fields via experimental and simulation data fusion

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npj Computational Materials 2024年第1期10卷 2512-2521页

作者： Sebastien Röcken Julija Zavadlav Department of Engineering Physics and Computation Multiscale Modeling of Fluid MaterialsTUM School of Engineering and DesignTechnical University of MunichMunichGermany Munich Data Science Institute Technical University of MunichMunichGermany

Machine Learning(ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level *** can be trained based on high-fidelity simulations or experiments,the former being the common ***,both approaches are impaired by scarce and erroneous data resulting in models that either do not agree with well-known experimental observations or are under-constrained and only reproduce some *** we leverage both Density Functional Theory(DFT)calculations and experimentally measured mechanical properties and lattice parameters to train an ML potential of *** demonstrate that the fused data learning strategy can concurrently satisfy all target objectives,thus resulting in a molecularmodel of higher accuracycompared to the models trained with a single data *** inaccuracies of DFT functionals at target experimental properties were corrected,while the investigated off-target properties were affected only mildly and mostly *** approach is applicable to any material and can serve as a general strategy to obtain highly accurate ML potentials.

关键词： properties satisfy attracting

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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