检索结果-内蒙古大学图书馆

Dual-Cloud Multi-Secret Sharing Architecture for Privacy Preserving Persistent Computation

IEEE Transactions on Information Forensics and Security 2024年 19卷 7523-7535页

作者： Chen, Yu-Chi Yang, Jhe-Kai Yen, Hsin-Chan Lin, Pei-Wen National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan Yuan Ze University Department of Computer Science and Engineering Taoyuan32003 Taiwan Genie Networks Taipei114064 Taiwan

With the prevalence of artificial intelligence, people collect data through numerous sensors and use machine learning to create models for intelligent services. However, data privacy and massive data issues are raised with the proliferation of devices. Although secret sharing can be the solution to providing privacy and performing efficient computations (than homomorphic encryption-based) in the privacy domain, sharing large amounts of data may impose a considerable storage burden on resource-restricted devices. Therefore, we aim to reduce the shares that participants need to hold and construct secure computation protocols that form privacy-preserving data utilization with multi-secret sharing. In this paper, we study the mechanism of persistent computation with multi-secret sharing for privacy protection, which avoids the requirements for participants to store extra information during secure computation. We present the mask techniques to convert secrets into protected data and upload them separately to non-colluding dual-cloud servers through multi-secret sharing. Then, we can locally evaluate the operation result shares by revealing a part of the protected data, where the true secret consistently remains secure as the other part stays in a shared state. Eventually, we outsource a dataset to the cloud, build a privacy-preserving multi-party kNN classification based on our scheme, and provide some experiments to demonstrate the feasibility and usability of storage size. © 2005-2012 IEEE.

关键词： computer architecture

来源：评论

学校读者我要写书评

暂无评论

Human Hand Gesture Classification Based on Surface Electromyography Signals by Using a Vector–Kernel Convolutional Takagi–Sugeno–Kang Neuro-fuzzy Classifier

引用

Sensors and Materials 2024年第2-3期36卷 623-637页

作者： Lin, Cheng-Jian Lin, Chun-Jung Lin, Xin-Wei Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Muscle–computer interfaces are devices that can identify the meaning of human bioelectrical signals, such as surface electromyography (sEMG) signals. sEMG signals can be obtained from arm-worn sensors and can be used to classify human gestures. In this paper, we propose a vector–kernel convolutional Takagi–Sugeno–Kang (TSK)-type neuro-fuzzy classifier (VK-CTNFC) to recognize human gestures represented by sEMG signals. First, vector–kernel convolution is used to extract the features of sEMG signals;this modification reduces the model parameters by half and increases the classification accuracy compared with those achieved with a conventional convolutional kernel. Second, the global average pooling method is used instead of the flattening method to improve feature fusion performance. Finally, a TSK-type neuro-fuzzy network is used for gesture classification. The publicly available dataset Ninapro DB1 was used in experiments for verifying the performance of the proposed VK-CTNFC. Data preprocessing was performed by wavelet denoising to smooth the sEMG waveform, and fast Fourier transform was used to convert time-domain sEMG signals into frequency-domain signals. Finally, the processed sEMG signals were input into the VK-CTNFC for training. The experimental results indicate that the proposed VK-CTNFC has an average accuracy of 87.18% and outperforms other reported methods. © MYU K.K.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Enhanced text-independent speaker recognition using MFCC, Bi-LSTM, and CNN-based noise removal techniques

引用

International Journal of Speech technology 2024年第4期27卷 1013-1026页

作者： Tiwari, Manish Verma, Deepak Kumar Department of Computer Science and Engineering University Institute of Engineering and Technology Chhatrapati Shahu Ji Maharaj University UP Kanpur India

This research article introduces a novel approach to text-independent speaker recognition by integrating Mel-Frequency Cepstral Coefficients (MFCC) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, with noise removal facilitated by Convolutional Neural Networks (CNNs). The primary objective is to upgrade the robustness and precision of speaker recognition systems in real-world environments where background noise is prevalent. The proposed method begins with the extraction of MFCC features, which effectively capture the timbral characteristics of the speech signal. To enhance these features, we employ a CNN-based noise removal mechanism that reduces background interference, thereby improving the quality of the input signal. The denoised MFCC features are then fed into a Bi-LSTM network, which excels in modeling temporal dependencies and capturing long-range contextual information inherent in speech data. Extensive experiments were conducted on publicly available datasets, demonstrating significant improvements in speaker recognition accuracy under various noise conditions compared to traditional approaches. The integration of CNN for noise removal and Bi-LSTM for temporal feature modeling showcases a synergistic effect, leading to a more robust and reliable speaker recognition system. Our results underscore the effectiveness of combining advanced feature extraction, noise reduction, and deep learning techniques for enhanced speaker recognition in challenging acoustic environments. The accuracy of the proposed method is found to be 98.17% at the Signal to Noise Ratio (SNR) level of 30 dB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Risk Factors Categorizations of Ischemic Heart Disease in south-Western Bangladesh

引用

Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

来源：评论

学校读者我要写书评

暂无评论

Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

引用

Tsinghua science and technology 2024年第1期29卷 86-98页

作者： Rui Yuan Shunmei Meng Ruihan Dou Xinna Wang Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210056China Faculty of Mathematics University of WaterlooWaterloo N2L 3G1Canada

Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network *** to recommend the most suitable candidate from these numerous available services is an urgent ***-through rate(CTR)prediction is a core task of traditional service ***,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing *** this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user *** uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term *** terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal *** experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

关键词： recommender system logarithmic network nonlocal network

来源：评论

学校读者我要写书评

暂无评论

Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Optimized deep learning-oriented strategy for predicting the location of delamination and detecting their shapes in composite structures

引用

Iran Journal of computer science 2025年第2期8卷 403-418页

作者： Demircioğlu, Ufuk Bakır, Halit Mechanical Engineering Department Sivas University of Science and Technology Sivas Türkiye Computer Engineering Department Sivas University of Science and Technology Sivas Türkiye

In this paper, the possibilities of deep learning approaches for sandwich structure delamination detection and prediction have been investigated. The research was divided into three parts: a validation study, a classification analysis of delamination shape, and delamination location forecasting. The best mesh configuration in terms of computational time and accuracy was chosen during the validation study by comparing a COMSOL MULTIPHYSICS model’s results with those obtained in a previously conducted related study. The natural frequencies of sandwich beams with various delamination forms and locations were collected from COMSOL MULTIPHYSICS and used in the classification and regression studies. Deep learning (DL) models were trained to predict the location of delamination and detect the shape of delamination using the obtained dataset. To improve accuracy and generalizability, we proposed tuning the hyperparameters of the model to choose the best DL structures for achieving both regression and shape detection tasks with as high performance as possible. The Bayesian algorithm was used to select the best combination of values for multiple hyperparameters used in the regression and classification models. Furthermore, several optimization phases have been held to explore the hyperparameter space carefully. In the optimization study, various regression and classification metrics were used to determine the optimum model. The outcomes demonstrate that both delamination location prediction and shape detection were accomplished with excellent performance by the optimized deep learning models. Particularly, a mean squared error value of 0.0000043303 was achieved, which allows regression analysis to identify the location of the delamination accurately. Likewise, 0.9944 of accuracy value has been achieved for the shape detection analysis, which allows the model to identify delamination shapes precisely. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2

关键词： Deep learning Finite element method Hyperparameter tuning Sandwich structures delamination

来源：评论

学校读者我要写书评

暂无评论

Optimizing XGBoost for Enhanced Electrical Theft Detection: A Fusion of Particle Swarm and Jaya Optimization Techniques 4

Optimizing XGBoost for Enhanced Electrical Theft Detection: ...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Prusty, Sashikanta Nayak, Debasish Swapnesh Kumar Moharana, Meena Prusty, Sushree Gayatri Priyadarsini Sahoo, Manmohan Rautaray, Jyotirmayee Department of Computer Science & Engineering Bhubaneswar India Centurion University of Technology & Management Department of Computer Science & Engineering Bhubaneswar India Department of Computer Science & Engineering Bhubaneswar India Odisha University of Technology & Research Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350383997

The most serious and hazardous for an electrical provider these days are non-technical losses caused due to electricity theft. The economy as a whole is impacted by fraudulent electricity usage, which lowers supply quality, raises generator load, and drives up costs for legal customers. To avoid these issues, this work proposes a smart electrical theft detection system using the XGBoost model combined with two optimization techniques Particle Swarm Optimization (PSO) and Jaya Optimization (JO). Furthermore, to handle the missing instances in the State Grid Corporation of China (SGCC) imbalanced dataset, we implemented a novel LORAS (Local Outlier-Robust Adaptive Synthetic) technique that balances the target class by making fake data points near the edges of different classes. Finally, the results show that the optimized XGBoost-PSO technique significantly classifies into two distinct categories (i.e. theft as '1' or no theft as '0') with having higher F1 Score (PSO Optimized): 0.9552 and AUC (PSO Optimized): 0.9667. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

A Novel Insertion Solution for the Travelling Salesman Problem

引用

computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun Department of Computer Science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria Department of Computing MiVA UniversityAbuja900211Nigeria Department of Computer Science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria Department of Computer Science University of ZululandKwadlangezwa3886South Africa Department of Computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria Department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania Department of Computer Science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

来源：评论

学校读者我要写书评

暂无评论

An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：