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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho school of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the school of Biomedical Engineering of Southern Medical University(SMU-dataset),and The national Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Adaptive-basis decomposition-based low-rank network for efficient non-uniform motion deblurring

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Optoelectronics Letters 2025年第1期21卷 43-50页

作者： CHEN Lei XIONG Qingbo ZHANG Wei LI Runde school of software Henan University school of Applied Technology China University of Labor Relations

In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components: a custom-designed dataset and a low-rank module(LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring(ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network(SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.

关键词： Image enhancement

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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia school of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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Adversarial Training for Supervised Relation Extraction

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Tsinghua Science and Technology 2022年第3期27卷 610-618页

作者： Yanhua Yu Kanghao He Jie Li school of Computer Science(national pilot software Engineering school) Beijing University of Posts and TelecommunicationsBeijing 100876China

Most supervised methods for relation extraction(RE) involve time-consuming human annotation. Distant supervision for RE is an efficient method to obtain large corpora that contains thousands of instances and various relations. However, the existing approaches rely heavily on knowledge bases(e.g., Freebase), thereby introducing data noise. Various relations and noisy labeling instances make the issue difficult to solve. In this study, we propose a model based on a piecewise convolution neural network with adversarial training. Inspired by generative adversarial networks, we adopt a heuristic algorithm to identify noisy datasets and apply adversarial training to RE. Experiments on the extended dataset of SemEval-2010 Task 8 show that our model can obtain more accurate training data for RE and significantly outperforms several competitive baseline models. Our model has an F1 score of 89.61%.

关键词： relation extraction piecewise convolution neural network adversarial training generative adversarial network

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Cross-project software defect prediction based on multi-source data sets

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 75-87页

作者： Huang Junfu Wang Yawen Gong Yunzhan Jin Dahai school of Computer Science(national pilot software Engineering school) Beijing University of Posts and TelecommunicationsBeijing 100876China

Cross-project defect prediction(CPDP) uses one or more source projects to build a defect prediction model and applies the model to the target project. There is usually a big difference between the data distribution of the source project and the target project, which makes it difficult to construct an effective defect prediction model. In order to alleviate the problem of negative migration between the source project and the target project in CPDP, this paper proposes an integrated transfer adaptive boosting(TrAdaBoost) algorithm based on multi-source data sets(MSITrA). The algorithm uses an existing two-stage data filtering algorithm to obtain source project data related to the target project from multiple source items, and then uses the integrated TrAdaBoost algorithm proposed in the paper to build a CPDP model. The experimental results of Promise’s 15 public data sets show that: 1) The cross-project software defect prediction model proposed in this paper has better performance in all tested CPDP methods;2) In the within-project software defect prediction(WPDP) experiment, the proposed CPDP method has achieved the better experimental results than the tested WPDP method.

关键词： cross-project defect prediction multi-source transfer adaptive boosting ensemble learning

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Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

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China Communications 2024年第5期21卷 261-279页

作者： Sun Yi Wang Zhouyang Beijing University of Posts and Telecommunications School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications International School

The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

关键词： fuzzy ordered regression(OR) relaxing variables twin support vector machine

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Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2137-2162页

作者： Lei Yan Maode Ma Dandan Li Xiaohong Huang Yan Ma Kun Xie school of Computer Science(national pilot software Engineering school) Beijing University of Posts and TelecommunicationsBeijingChina College of Engineering QatarUniversityDohaQatar

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate *** methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on *** these existing mechanisms,the identification of malicious flows depends on the IP ***,the IP address is easy to be changed by *** the IP address,the certificate ismore challenging to be tampered with or ***,the traffic trend in the network is towards *** certificates are popularly utilized by IoT devices for authentication in encryption *** proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN *** on DTLShps,the SDN controller can collect statistics on *** this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for *** goal is ***,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire ***,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay ***,for detecting the Crossfire attack accurately,a method based on graph connectivity is ***,several trust-based routing principles are proposed tomitigate the Crossfire *** principles can also encourage users to use certificates in *** performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting ***,our trust model has a more appropriate decay rate than the traditional methods.

关键词： Trust model certificate SDN Crossfire attack bayesian trust system forgetting curve IoT

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GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

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Computers, Materials & Continua 2020年第8期64卷 925-940页

作者： Jinyuan Li Hao Li Guorong Cui Yan Kang Yang Hu Yingnan Zhou national pilot school of software Yunnan UniversityKunming650091China school of Engineering and Applied Science The George Washington UniversityWashington DC20052USA

With continuous urbanization,cities are undergoing a sharp expansion within the regional *** to the high cost,the prediction of regional traffic flow is more difficult to extend to entire urban *** address this challenging problem,we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet,which predicts traffic flow of surrounding areas based on inflow and outflow information in central *** method is data-driven,and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional *** introduce adversarial training to improve performance of prediction and enhance the *** generator mainly consists of two parts:abstract traffic feature extraction in the central region and traffic prediction in the extended *** particular,the feature extraction part captures nonlinear spatial dependence using gated convolution,and replaces the maximum pooling operation with dynamic routing,finally aggregates multidimensional information in capsule *** effectiveness of the method is evaluated using traffic flow datasets for two real traffic networks:Beijing and New *** on highly challenging datasets show that our method performs well for this task.

关键词： Regional traffic flow adversarial training feature extraction nonlinear spatial dependence dynamic routing

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On UAV Serving Node Deployment for Temporary Coverage in Forest Environment:A Hierarchical Deep Reinforcement Learning Approach

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Chinese Journal of Electronics 2023年第4期32卷 760-772页

作者： WANG Li WU Xuewei WANG Yanhui XIAO Zhe LI Liang FEI Aiguo school of Computer Science (national pilot software Engineering school) Beijing University of Posts and Telecommunications school of Electronic Engineering Beijing University of Posts and Telecommunications

Unmanned aerial vehicles(UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station or an available base station. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning(DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL(HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.

关键词： Deep learning Base stations Simulation Clustering algorithms Forestry Reinforcement learning Autonomous aerial vehicles

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MEM-TET: Improved Triplet Network for Intrusion Detection System

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Computers, Materials & Continua 2023年第7期76卷 471-487页

作者： Weifei Wang Jinguo Li Na Zhao Min Liu College of Computer Science and Technology Shanghai University of Electric PowerShanghai201306China national pilot school of software Yunnan UniversityKunming650504China

With the advancement of network communication technology,network traffic shows explosive ***,network attacks occur *** intrusion detection systems are still the primary means of detecting ***,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered ***,this study proposes a unique deep learning-based intrusion detection *** use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training *** the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to ***,the distance relationship between the triples determines whether the traffic is an *** addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature *** proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware Dataset(AAGM17),Knowledge Discovery and Data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and Data Mining(NSL-KDD)*** achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.

关键词： Intrusion detection memory-augmented autoencoder deep metric learning imbalance data

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