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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

computer Modeling in engineering & sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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An Improved Hybrid Deep Learning Approach for Security Requirements Classification

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computers, Materials & Continua 2025年第3期82卷 4041-4067页

作者： Shoaib Hassan QianmuLi Muhammad Zubair Rakan AAlsowail Muhammad Umair School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Faculty of Information Technology and Computer Science University of Central PunjabLahore5400Pakistan Computer Skills Self-Development Skills DevelopmentDeanship of Common First YearKing Saud UniversityRiyadh11362Saudia Arabia

As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics

关键词： Requirements engineering security requirements deep learning CNN XGBoost classification

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Effect of heat treatment on the microstructure and mechanical properties of a multidirectionally forged Mg-Gd-Y-Zn-Zr-Ag alloy

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Journal of Magnesium and Alloys 2023年第6期11卷 2042-2053页

作者： Junkai Wang Chuming Liu Shunong Jiang Yingchun Wan Zhiyong Chen School of Materials Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory for Powder Metallurgy Central South UniversityChangsha 410083China School of Materials Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Multidirectional forging(MDF)was successfully applied to fabricate large-size Mg-Gd-Y-Zn-Zr-Ag alloy in this work and effects of T4,T5 and T6 treatments on the microstructure and mechanical properties of the as-forged alloy were *** show that dynamic recrystallization(DRX)occurs and second phase particles precipitate along the grain boundary during the MDF *** annealing treatment(T4),the volume fraction and size of dynamic precipitates slightly increase at a lower temperature(430℃)compared with those of MDFed sample,while they are dissolved into theα-Mg matrix at a higher temperature(450℃).At the meantime,short plate-shaped long-period stacking ordered(LPSO)phases are observed in the DRX grains of the MDFed sample and then dissolved into theα-Mg matrix during annealing at both *** basal texture is identified in the MDFed sample,but the basal pole tilts away from final forging direction and rare-earth texture component with<1121>orientation parallel to penultimate forging direction becomes visible after *** T6 sample annealing at 430℃for 4 h and ageing at 200℃for 34 h exhibits the superior strength and ductility in this *** ultimate tensile strength,tensile yield strength and elongation to failure,which is 455 MPa,308 MPa and 7.7%,respectively,are overall improved compared with the directly-aged(T5)*** paper provides a superior heat treatment schedule to manufacture high-performance large-scale Mg-Gd-Y-Zn-Zr-Ag components for industrial production.

关键词： Mg-Gd-Y-Zn-Zr-Ag alloy Multidirectional forging Dynamic precipitation Heat treatment Mechanical properties

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A G-banded Chromosome Image Enhancement Algorithm Based on B-Spline Curves

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Signal, Image and Video Processing 2025年第8期19卷 1-10页

作者： Wang, Hui Feng, Shengri Yu, Tiantian School of Electronic Science and Engineering Hunan University of Information Technology Changsha410151 China Zoomlion Heavy Industry Science & Technology Co. Ltd. Changsha410013 China

To address the issue that the low contrast of human G-banded metaphase chromosome optical microscopic images hinders chromosome karyotype analysis, a chromosome band region enhancement algorithm based on B-spline curves is proposed. The method integrates a multi-level threshold segmentation algorithm, exhaustively searches for optimal control point parameters by maximizing joint similarity as the optimization objective, and constructs a grayscale mapping table using cubic clamped B-spline curves. Experimental results, with peak signal-to-noise ratio as the cross-validation index, demonstrate that the proposed algorithm achieves enhancement effects closer to the benchmark reference image compared to traditional gamma transformation and contrast-limited adaptive histogram equalization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image enhancement

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer science 2024年第5期5卷 567页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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A Ca-substituted air-stable layered oxide cathode material with facilitated phase transitions for high-performance Na-ion batteries

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Rare Metals 2024年第11期43卷 5701-5711页

作者： Shu-Bing Wei Yong-Ju He Yan Tang Hong-Wei Fu Jiang Zhou Shu-Quan Liang Xin-Xin Cao School of Materials Science and Engineering Central South UniversityChangsha 410083China School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Key Laboratory of Electronic Packaging and Advanced Functional Materials of Hunan Province Central South UniversityChangsha 410083China School of Physics and Electronics Hunan UniversityChangsha 410082China

Cost-efficient and high-performance cathodes play a crucial role in the advancement of grid-sc ale sodium-ion batteries(SIBs).As promising high-capacity cathode materials for SIBs,O_(3)-type Na-based layered transition metal oxides constantly experience inadequate air stability and complex phase ***,a comprehensive investigation was conducted to explore the impact of Ca substitution on both the electrochemical performance and structural stability of O_(3)-Na_(1-2x)Ca_xNi_(0.25)Fe_(0.5)Mn_(0.25)O_(2)(x=0.0.02 and 0.05)that is *** proper Ca content,O_(3)-type Na_(0.96)Ca_(0.02)-Ni_(0.25)Fe_(0.5)Mn_(0.25)O_(2)can deliver a reversible capacity of122.1 mAh·g^(-1)at 10 mA·g^(-1),with capacity retention of83.4%after 200 cycles at 50 mA·g^(-1)and good rate *** air sensitivity is investigated,and its potential as the Na host in full cells has been *** situ X-ray diffraction reveals the facilitated O_(3)-P_(3)-O_(3)phase transitions of such cathode during the whole electrochemical *** a simple and effective strategy may reveal a new avenue for high-stable O_(3)-type cathodes and promote the practical applications of SIBs.

关键词： Sodium-ion batteries O_(3)-type layered oxides Calcium-substitution Air-stable

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Analyzing the performance of geometric mean optimization-based artificial neural networks for cryptocurrency forecasting

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Behera, Sudersan Kumar, A V S Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN, JPSNN, FLANN, RBFN, and MLP, thereby creating six hybrid models. Later on, we engage all these hybrid models to predict the closing price of four widely used cryptocurrencies. For comparison purposes, we also engage the widely popular traditional GD algorithm to fix the hidden layer input weights and biases of the same set of ANNs, creating six alternate models and employing those models for the same prediction task. In order to assess the effectiveness of each model, we employ the mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) statistics. The experimental result shows the superiority of GMO-based PSNN over others. We then compare the GMO-based PSNN's outcomes with some of the existing hybrid models in the literature. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN Cryptocurrency forecasting Financial time series forecasting GMO Higher order neural networks

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ArchSentry: Enhanced Android Malware Detection via Hierarchical Semantic Extraction

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2822-2837页

作者： Wang, Tianbo Liu, Mengyao Li, Huacheng Zhao, Lei Jiang, Changnan Xia, Chunhe Cui, Baojiang Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Computer Science and Engineering Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Android malware

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An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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