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2023 IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Cai, Yongheng Zhou, Zhuye Li, Zehui School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350309614

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R2), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios. © 2023 IEEE.

关键词： BP neural network genetic algorithm optimization algorithm

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Predicting Gasoline RON Loss by Machine Learning 6

Predicting Gasoline RON Loss by Machine Learning

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6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022

作者： Liu, Limin Chen, Zhen Wang, Yongsheng Liu, Guangwen Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service College of Data Science and Application Hohhot China Inner Mongolia University of Technology College of Data Science and Application Hohhot China

ISBN: (数字)9781665469869

ISBN: (纸本)9781665469869

In order to improve gasoline quality and reduce the environmental pollution caused by gasoline combustion, we aim to study the nonlinear relationship between octane loss and various gasoline components in gasoline refining process, further optimize the gasoline refining process, and reduce the content of sulfur and olefin in gasoline products while minimizing the octane loss. Firstly, we used the nonlinear data reduction algorithm t-SNE to preprocess the data of each component in gasoline and clarify each characteristic parameter;then we constructed the Multiple Nonlinear Regression (MNR) model to analyze the correlation and influence of each characteristic data on octane value;finally, we selected NSGA-II multi-objective genetic algorithm. The NSGA-II multi-objective genetic algorithm was selected for in-depth optimization of the model. The multiple linear regression model was also constructed for comparison experiments, and the advantages and disadvantages of the predicted results of the two models were analyzed, as well as the feasibility of extension. In the experiments with industrial data samples published in Sinopec Gaoqiao Petrochemical real-time database (Honeywell PHD) and LIMS experimental database, it was found that the multivariate nonlinear regression model has more superiority in achieving the purpose of the study, the value of harmful content in gasoline was reduced by 32.7%, and the predicted data of each component of gasoline was more in line with environmental protection requirements. Therefore, the multivariate nonlinear model has the highest reasonableness in predicting the octane loss values. © 2022 IEEE.

关键词： Machine learning

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Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

arXiv

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arXiv 2024年

作者： Attota, Dinesh Chowdary Yarmohammadtoosky, Sahar Department of Data Science & Analytics College of Computer Science and Software Engineering Kennesaw State University MariettaGA United States

As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk. Copyright © 2024, The Authors. All rights reserved.

关键词： Logistic regression

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Siddiqi, Hafied Nur Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Technology PO Box 84428 Riyadh11671 Saudi Arabia Shenzhen University College of Computer Science & Software Engineering Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Prince Sultan University EIAS Data Science and Blockchain Laboratory CCIS Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a security mechanism that exploits the unique, unrepeatable physical characteristics of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The unique identification of IoT devices along the supply chain is implemented by using PUFs as tamper-resistant IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its potential to combat several attack scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing

关键词： Supply chain management

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing*

Group Signature with Time-Bound Keys for Secure E-health Rec...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junze Lu Chunqiang Hu Conghao Ruan Bin Cai Tao Xiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient.

关键词： Buildings Solids Security Time factors Internet of Things Bioinformatics

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A motion recognition model for upper-limb rehabilitation exercises

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16795-16805页

作者： Li, Qiaoqin Liu, Yongguo Zhu, Jiajing Chen, Zhi Liu, Lang Yang, Shangming Li, Juan Jin, Rongjiang Tao, Jing Chen, Lidian Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China No. 4 Section 2 Jianshe North Road Sichuan Chengdu610054 China College of Health Preservation and Rehabilitation Chengdu University of Traditional Chinese Medicine Chengdu610075 China College of Rehabilitation Medicine Fujian University of Traditional Chinese Medicine Fuzhou350122 China

In recent years, autonomous rehabilitation training systems with no or reduced supervision have attracted much research attentions. It is essential to automatically record and recognize motions so as to realize autonomous rehabilitation training. In this paper, we propose a motion recognition model of upper-limb rehabilitation exercise (MRURE) for post-strokes. Wearable inertial measurement unit (IMU) is used to collect motion data including tri-axial acceleration and angular velocity. A Mahalanobis distance-based dynamic time warping (MDDTW) technique and an improved metric learning algorithm Nearest Neighbors Constraint (NNC) are built to measure the similarity between motions. Then, the kNN classifier is adopted to classify motions based on Mahalanobis distance. In order to verify and validate the proposed motion recognition model, we conduct extensive experiments by motion samples collected from healthy participants. The overall recognition accuracy for five motions achieves 99.34%, which demonstrates that MRURE is effective for motion recognition of upper-limb exercises. The performance of MRURE is also compared with kNN based on Euclidean and Manhattan distance and the state-of-the-art motion recognition methods. Experiment results show that the accuracy of kNN based on Mahalanobis distance is better than that of Euclidean and Manhattan distance, and MRURE outperforms methods based on feature extraction. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Learning algorithms

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Semantic consistency assisted change detection in remote sensing images

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National Remote Sensing Bulletin 2025年第4期29卷 1031-1045页

作者： Wu, Hucheng Wang, Renfang Qiu, Hong Wang, Feng Gao, Guang Wu, Dun College of Computer Science and Technology Ocean University of China Qingdao266100 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Co. Ltd. Ningbo315042 China

Semantic change detection of remote sensing images plays an important role in ecological environment, land use, and land cover monitoring. In recent years, deep learning-based change detection methods are the hotspot of remote sensing intelligent interpretation concern;however, the existing three-branch semantic change detection methods lack consistency of change branch and semantic branches, which leads to the self-contradiction of the bitemporal semantic change detection. To address this problem, this study proposes a remote sensing image semantic change detection algorithm based on Siamese CNN and Transformer. In the encoding stage, the Siamese ResNet34 network is first designed to extract the multiscale features of the image, and the difference enhancement module is embedded to increase the attention of the change information;then, the semantic tokenizer is used to map the feature map into compact semantic tokens, and the Transformer encoder is used to combine the bitemporal semantics and the change information to model the"semantic change"consistency. In the decoding stage, different fine-grained semantic information is fused by the Transformer decoder using a hopping connection to generate a refined semantic feature map. Finally, the result of bitemporal semantic change is obtained after upsampling recovery and mask multiplication. Experimental results on the remote sensing semantic change detection public dataset SECOND and Landsat-SCD show that the proposed algorithm can effectively focus on the change region, maintain the consistency between the change results and the semantic results, and achieve excellent evaluation indexes and visual effects. We can draw the following conclusions: (1) The proposed difference enhancement module can enhance the difference characteristics of bitemporal remote sensing images and improve the network’s focus on change information. (2) The proposed bitemporal Transformer module maps the difference information and bitemporal sem

关键词： Change detection

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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A Novel AI-based Hybrid Ensemble Segmentation CAD System for Lumber Spine Stenosis pathological Regions Using MRI Axial Images 8

A Novel AI-based Hybrid Ensemble Segmentation CAD System for...

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8th International Artificial Intelligence and data Processing Symposium, IDAP 2024

作者： Salem, Saied Mukhlis, Raza Katar, Oguzhan Ertugrul, Bilal Yildirim, Ozal Al-Antari, Mugahed A. Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of Firat University Faculty of Technology Department of Software Engineering Elazig Turkey Firat University Faculty of Medicine Department of Neurosurgery Elaziǧ Turkey

ISBN: (纸本)9798331531492

Lumbar Spinal Stenosis (LSS) is a condition where the spinal canal in the lower back becomes narrower, resulting in pressure on the spinal cord and nearby nerves. To predict LSS, neurologists focus on four key regions in MRI axial scans: the Intervertebral Disc (IVD), Thecal Sac (TS), Posterior Element (PE), and the Area between the Anterior and Posterior (AAP) vertebrae. To delineate these regions, we present a novel hybrid ensemble deep learning CAD system for multi-class segmentation, encompassing data collection, preprocessing, automated hyperparameter optimization and selection, segmentation ensemble learning, and both qualitative and quantitative performance evaluation. The core of the proposed pipeline features two ensemble phases: (1) ensemble learning through feature space fusion of three distinct AI models (SwinUNETR, Attention Unet, and UNet++), and (2) ensemble learning via a stacking method of the feature maps outputs from the same AI models used in phase 1. The Mendeley benchmark dataset is employed to train and assess both CAD systems, while a private dataset from Firat University Hospital in Turkey is used for verification and validation. The best achieved results via FFA's mean dice score is 93.1% and mean IoU is 87.76%. showing the usefulness of using ensemble methods to capture different features from the trained models. © 2024 IEEE.

关键词： Image segmentation

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QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

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