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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

computer Modeling in engineering & sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Active learning Online learning Malicious traffic Concept drift Data imbalance

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Regulation of Zn2+solvation shell by a novel N-methylacetamide based eutectic electrolyte toward high-performance zinc-ion batteries

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材料科学技术（英文版） 2025年第8期211卷 53-61页

作者： Yong Liu Kaijia Feng Junmei Han Fei Wang Yibo Xing Feng Tao Haoming Li Binrui Xu Jiangtao Ji Hongxia Li School of Materials Science and Engineering Provincial and Ministerial Co-construction of Collaborative Innovation Center for Non-ferrous Metal New Materials and Advanced Processing TechnologyHenan University of Science and TechnologyLuoyang 471023PR China Faculty of Engineering Huanghe Science & Technology UniversityZhengzhou 450063PR China School of Information Engineering Henan University of Science and TechnologyLuoyang 471023PR China College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471023PR China State Key Laboratory of Advanced Refractories Sinosteel Luoyang Institute of Refractories Research Co.Ltd.Luoyang 471039PR China

Aqueous Zn-ion batteries(AZIBs)have been regarded as promising alternatives to Li-ion batteries due to their advantages,such as low cost,high safety,and environmental ***,AZIBs face significant challenges in limited stability and lifetime owing to zinc dendrite growth and serious side reactions caused by water molecules in the aqueous electrolyte during *** address these issues,a new eutectic electrolyte based on Zn(ClO4)2·6H2O-N-methylacetamide(ZN)is proposed in this *** with aqueous electrolyte,the ZN eutectic electrolyte containing organic N-methylacetamide could regulate the solvated structure of Zn2+,effectively suppressing zinc dendrite growth and side *** a result,the Zn//NH4V4O10 full cell with the eutectic ZN-1-3 electrolyte demonstrates signif-icantly enhanced cycling stability after 1000 cycles at 1 A ***,this study not only presents a new eutectic electrolyte for zinc-ion batteries but also provides a deep understanding of the influence of Zn2+solvation structure on the cycle stability,contributing to the exploration of novel electrolytes for high-performance AZIBs.

关键词： Zinc-ion batteries Eutectic electrolyte Zn dendrites Suppressed side reactions Zn2+solvation structure Electrochemical performance

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

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computers, Materials & Continua 2024年第7期80卷 1425-1440页

作者： Bingyi Ren Juwei Zhang Tong Wang School of Mechanical Engineering Henan University of Science and TechnologyLuoyang471000China School of Information Engineering Henan University of Science and TechnologyLuoyang471000China School of Electronic Information Zhengzhou Institute of Aeronautical Industry ManagementZhengzhou450046China Henan Province New Energy Vehicle Power Electronics and Power Transmission Engineering Research Center Luoyang471000China

Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important *** target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more ***,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion *** reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel *** addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous *** terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different ***,our proposed improved method shows excellent results on the TT100K *** with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.

关键词： Small target detection YOLOv7 traffic sign detection regression loss

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DeepSeqCoco: A Robust Mobile Friendly Deep Learning Model for Detection of Diseases in Cocos Nucifera

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IEEE Access 2025年 13卷 89367-89385页

作者： Daga, Miit Parikh, Dhriti Ramu, Swarna Priya Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore632014 India

Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification methods are manual, labor-intensive, and non-scalable. In response to these limitations, we come up with DeepSeqCoco, a deep learning based model for accurate and automatic disease identification from coconut tree images. The model was tested under various optimizer settings, such as SGD, Adam, and hybrid configurations, to identify the optimal balance between accuracy, minimization of loss, and computational cost. Results from experiments indicate that DeepSeqCoco can achieve as much as 99.5% accuracy (achieving up to 5% higher accuracy than existing models) with the hybrid SGD then Adam showing the lowest validation loss of 2.81%. It also shows a drop of up to 18% in training time and up to 85% in prediction time for input images. The results point out the promise of the model to improve precision agriculture through an AI-based, scalable, and efficient disease monitoring system. © 2013 IEEE.

关键词： Precision agriculture

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Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

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Critical Reviews in Biomedical engineering 2024年第2期52卷 39-50页

作者： Wang, Xue Song, Zhili Zhu, Jianlin Li, Zhixiang School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov–Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences. © 2023 by Begell House, Inc.

关键词： Magnetic resonance imaging

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A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

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International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

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An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

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Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

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Multi-layer collaborative optimization fusion for semi-supervised learning

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Chinese Journal of Aeronautics 2023年第11期36卷 342-353页

作者： Quanbo GE Muhua LIU Jianchao ZHANG Jianqiang SONG Junlong ZHU Mingchuan ZHANG School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China Qiandao Lake Institute of Science Hangzhou 311799China

Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image ***,traditional CTA approaches face challenges such as high computational complexity and low classification *** overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification ***,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training ***,we conduct various experiments to verify the performance of the proposed *** results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.

关键词： Collaborative training Fusion Image classification K-means algorithm Semi-supervised learning

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