Automatika最新文献_第3页

A multi-modal integrated deep neural networks for the prediction of cardiovascular disease in type-2 diabetic males 多模态集成深度神经网络预测2型糖尿病男性心血管疾病

4区计算机科学

Automatika Pub Date : 2023-10-02 DOI: 10.1080/00051144.2023.2269515

S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi

{"title":"A multi-modal integrated deep neural networks for the prediction of cardiovascular disease in type-2 diabetic males","authors":"S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi","doi":"10.1080/00051144.2023.2269515","DOIUrl":"https://doi.org/10.1080/00051144.2023.2269515","url":null,"abstract":"Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Almost sure stability of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals 具有确定性和随机切换信号的Caputo分数阶切换线性系统的几乎肯定稳定性

4区计算机科学

Automatika Pub Date : 2023-10-02 DOI: 10.1080/00051144.2023.2262016

Qixiang Wang, Fei Long, Lipo Mo, Jing Yang

引用次数: 0

Piecewise linear approximation for identifying wind power ramp events 分段线性逼近法辨识风力斜坡事件

4区计算机科学

Automatika Pub Date : 2023-10-02 DOI: 10.1080/00051144.2023.2241772

J. Jayalakshmi, M. Mary Linda

引用次数: 0

Dynamic low power management technique for decision directed inter-layer communication in three dimensional wireless network on chip 片上三维无线网络决策导向层间通信的动态低功耗管理技术

4区计算机科学

Automatika Pub Date : 2023-10-02 DOI: 10.1080/00051144.2023.2261088

T. R. Dinesh Kumar, A. Karthikeyan

{"title":"Dynamic low power management technique for decision directed inter-layer communication in three dimensional wireless network on chip","authors":"T. R. Dinesh Kumar, A. Karthikeyan","doi":"10.1080/00051144.2023.2261088","DOIUrl":"https://doi.org/10.1080/00051144.2023.2261088","url":null,"abstract":"3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seven levels highly efficient modular multilevel matrix converter (M3C) for low frequency three-phase AC-AC conversion 七电平高效模块化多电平矩阵变换器(M3C)，用于低频三相交流-交流转换

4区计算机科学

Automatika Pub Date : 2023-09-20 DOI: 10.1080/00051144.2023.2253067

V. Karpagam, N. Narmadhai

引用次数: 0

Detection of glioma on brain MRIs using adaptive segmentation and modified graph neural network based classification 基于自适应分割和改进图神经网络分类的脑胶质瘤mri检测

4区计算机科学

Automatika Pub Date : 2023-09-20 DOI: 10.1080/00051144.2023.2256521

V. Nagasumathy, B. Paulchamy

{"title":"Detection of glioma on brain MRIs using adaptive segmentation and modified graph neural network based classification","authors":"V. Nagasumathy, B. Paulchamy","doi":"10.1080/00051144.2023.2256521","DOIUrl":"https://doi.org/10.1080/00051144.2023.2256521","url":null,"abstract":"Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm 基于优化分割方案和监督算法的乳腺癌计算机辅助诊断系统

4区计算机科学

Automatika Pub Date : 2023-09-19 DOI: 10.1080/00051144.2023.2244307

S. Balaji, T. Arunprasath, M. Pallikonda Rajasekaran, G. Vishnuvarthanan, K. Sindhuja

引用次数: 0

An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks 基于激励的认知无线电网络动态节能频谱分配

4区计算机科学

Automatika Pub Date : 2023-09-15 DOI: 10.1080/00051144.2023.2246810

Poornima Pandian, Chithra Selvaraj

引用次数: 0

Phase space load balancing priority scheduling algorithm for cloud computing clusters 云计算集群的相空间负载均衡优先级调度算法

4区计算机科学

Automatika Pub Date : 2023-09-11 DOI: 10.1080/00051144.2023.2254981

Zhou Zheng

{"title":"Phase space load balancing priority scheduling algorithm for cloud computing clusters","authors":"Zhou Zheng","doi":"10.1080/00051144.2023.2254981","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254981","url":null,"abstract":"Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Searchable encryption algorithm in computer big data processing application 可搜索加密算法在计算机大数据处理中的应用

4区计算机科学

Automatika Pub Date : 2023-09-11 DOI: 10.1080/00051144.2023.2254978

Lu Ming

{"title":"Searchable encryption algorithm in computer big data processing application","authors":"Lu Ming","doi":"10.1080/00051144.2023.2254978","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254978","url":null,"abstract":"With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0