Automatika最新文献_第7页

GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning GBDTMO:作为机器学习早期乳腺癌检测和分类的新选择

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-27 DOI: 10.1080/00051144.2023.2226946

Vibith A. S., Jobin Christ M C

{"title":"GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning","authors":"Vibith A. S., Jobin Christ M C","doi":"10.1080/00051144.2023.2226946","DOIUrl":"https://doi.org/10.1080/00051144.2023.2226946","url":null,"abstract":"Breast cancer is the second leading cause of disease death in women, after lung and bronchus cancer. According to measurements, mammography misses breast cancer in 10% to 15% of cases for women aged 50 to 69 years. In the current study, we used the Wisconsin breast cancer dataset to develop a two-stage model for breast cancer diagnosis. The main goal of this study effort is to effectively carry out feature selection and classification tasks. Gradient Boosting Decision Tree-based Mayfly Optimisation (GBDTMO), an innovative and efficient breast cancer diagnostic machine learning system, is provided. In the second stage, we employ a Mayfly search to determine which subset of traits is the best. Two more well-known datasets on breast cancer, the ICCR and the Cancer Corpus, were also compared for classification accuracy. The accuracy of the suggested GBDTMO model was higher than that of the existing GBDT and Practical Federated Gradient Boosting Decision Tree (PFGBDT), which had accuracy values of 93.25% and 94.25%, respectively. Similarly, the recall, F-measure, and ROC area values were 98.52%, 97.52%, and 96.32%, respectively. Furthermore, it demonstrated a lower RMSE of 0.98 than the existing GBDT and PFGBDT.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48599041","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

Optimization of virtual machines performance using fuzzy hashing and genetic algorithm-based memory deduplication of static pages 基于模糊哈希和遗传算法的虚拟机性能优化

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-27 DOI: 10.1080/00051144.2023.2223479

N. Jagadeeswari, V. Mohanraj, Y. Suresh, J. Senthilkumar

{"title":"Optimization of virtual machines performance using fuzzy hashing and genetic algorithm-based memory deduplication of static pages","authors":"N. Jagadeeswari, V. Mohanraj, Y. Suresh, J. Senthilkumar","doi":"10.1080/00051144.2023.2223479","DOIUrl":"https://doi.org/10.1080/00051144.2023.2223479","url":null,"abstract":"The demand for memory capacity has increased, and cloud energy usage has soared. The performance and scalability of virtualization interfaces in cloud computing are hampered by a lack of sufficient memory. To figure out this problem, a technique defined as memory deduplication is widely used to reduce memory consumption utilizing the page-sharing method. However, this method of memory deduplication using KSM has significant drawbacks, such as overhead owing to many online comparisons, which will consume so many CPU resources. In this research, a modified approach of Memory Deduplication of Static Memory Pages (mSMD), which is based on the identification of similar applications by Fuzzy hashing and clustering them using the Hierarchical Agglomerative Clustering approach, followed by similarity detection between static memory pages based on Genetic Algorithm and details stored in Multilevel shared page table, both operations performed in offline and final memory deduplication is carried out during online, is proposed for achieving performance optimization in virtual machines by reducing memory capacity requirements. When compared to existing techniques, the simulation results indicate that the proposed approach mSMD efficaciously minimizes the memory capacity required while improving performance.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42048660","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: 1

Automated program and software defect root cause analysis using machine learning techniques 自动化程序和软件缺陷的根本原因分析使用机器学习技术

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-27 DOI: 10.1080/00051144.2023.2225344

C. Anjali, Julia Punitha Malar Dhas, J. Amar Pratap Singh

引用次数: 0

Deep learning model for traffic flow prediction in wireless network 无线网络交通流量预测的深度学习模型

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-21 DOI: 10.1080/00051144.2023.2220203

A. Kavitha, S. Mary Praveena

引用次数: 0

Enhanced EEG classification using adaptive DWT and heuristic-ICA algorithm 采用自适应小波变换和启发式ica算法增强脑电分类

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-13 DOI: 10.1080/00051144.2023.2220207

P. Visu, P. Smitha, M. Velayutham, Mohd Wazih Ahmad

{"title":"Enhanced EEG classification using adaptive DWT and heuristic-ICA algorithm","authors":"P. Visu, P. Smitha, M. Velayutham, Mohd Wazih Ahmad","doi":"10.1080/00051144.2023.2220207","DOIUrl":"https://doi.org/10.1080/00051144.2023.2220207","url":null,"abstract":"Electroencephalography (EEG) signals contain important information about the inner functioning of the brain. Effective extraction of this information will help in the detection of brain-related health conditions and emotions of a person or it can also be used as a communication medium between humans and machines. In our proposed system, we introduced Adaptive DWT by combining the temporal resolution capability of DWT, with the special capability of Fourier transform to remove the artefacts in the signal. This is achieved by using an adaptive thresholding function rather than hard or soft thresholding to improve the quality parameters of the signal. The proposed filtering model has improved the Signal to Noise ratio when compared to traditional filtering techniques. EEG features are extracted with the help of Heuristic-Independent Component Analysis (ICA) by applying covariance to equalize or improve the data. The main drawback with the existing CNN algorithm is gradient vanishing during training, this reduces the overall performance of the algorithm during classification. Therefore, using the memory function to store the previous value of iteration improves the classification accuracy and reduces the gradient vanishing problem. The proposed technique is found to have better accuracy of about 98% in classifying autism and epilepsy datasets.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44823544","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

AI-based performance optimization of MPTT algorithms for photovoltaic systems 基于ai的光伏系统MPTT算法性能优化

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-13 DOI: 10.1080/00051144.2023.2222251

K. J. Nigel, R. Rajeswari

{"title":"AI-based performance optimization of MPTT algorithms for photovoltaic systems","authors":"K. J. Nigel, R. Rajeswari","doi":"10.1080/00051144.2023.2222251","DOIUrl":"https://doi.org/10.1080/00051144.2023.2222251","url":null,"abstract":"ABSTRACT Solar models have been drawing much attention in the contemporary electricity environment. Solar energy installations employ various MPPT techniques that generate the most energy. Increasing a solar (PV) device's energy effectiveness has become a key concern for scientists. Multiple MPPT approaches that collect the most power possible using a PV array have been researched. Both primary and intermediate-type procedures will be used in most procedures. The performance and convergence velocity of such a PV device become significant depending on its practical deployment under various conditions. The energy attributes of unit sections collectively serve as the primary energy-extracting elements in specific systems, dependent upon all interior and exterior elements. Considering specific external dynamical circumstances, traditional maximal power point tracing systems will not have the required translation efficacy. For assessing the overall effectiveness of the proposed intelligent maximal power point outlining methodology in partially shaded situations having significant and dynamical variations within ambient parameters, that study contrasts its efficacy using traditional maximal power point tracing techniques.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46681961","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

A novel MPPT design for a wind energy conversion system using grey wolf optimization 一种基于灰狼优化的风能转换系统MPPT设计方法

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-09 DOI: 10.1080/00051144.2023.2218168

G. Rashmi, M. Linda

引用次数: 1

Novel computer aided diagnostic system using hybrid neural network for early detection of pancreatic cancer 基于混合神经网络的新型计算机辅助诊断系统用于癌症的早期检测

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-07 DOI: 10.1080/00051144.2023.2219099

T. Thanya, Wilfred Franklin S

{"title":"Novel computer aided diagnostic system using hybrid neural network for early detection of pancreatic cancer","authors":"T. Thanya, Wilfred Franklin S","doi":"10.1080/00051144.2023.2219099","DOIUrl":"https://doi.org/10.1080/00051144.2023.2219099","url":null,"abstract":"One of the most dangerous tumours in the world, pancreatic cancer (PC), has an unimpressive five-year survival rate of about 5%. An early PC identification is crucial for raising patient survival rates. Diagnosis of PC requires computed tomography (CT), magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP), or biopsy. The proposed CAD design approach includes image preprocessing, segmentation, feature extraction, and classification phases. Preprocessing is done by using Colour conversion and an isotropic diffusion filter approaches. After that, proposed Fuzzy K-NN Equality algorithm used in segmentation procedures. Deep Learning with feature extraction is used as a classification tool. Tumour cells are classified using the features collected from the pancreatic sample. Train values and testing datasets are part of the image classification criterion. For the purpose of detecting pancreatic cancer, a hybrid Deep Convolutional Neural Network with Deep Belief Network (DCNN_DBN) algorithm is used. According to the experimental findings, the current CAD system offers massive prospects as well as safety in the automated diagnosis of both benign as well as malignant cancers and produces the accuracy of 99.6%. Using this classifier, computing complexity is massively diminished. The suggested technique could be enhanced to detect more pancreatic cancer cell abnormalities.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43657712","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: 1

Optimal progressive classification study using SMOTE-SVM for stages of lung disease 基于SMOTE-SVM的肺部疾病分期最优渐进分类研究

IF 1.9 4区计算机科学

Automatika Pub Date : 2023-06-07 DOI: 10.1080/00051144.2023.2218167

R. Sujitha, B. Paramasivan

{"title":"Optimal progressive classification study using SMOTE-SVM for stages of lung disease","authors":"R. Sujitha, B. Paramasivan","doi":"10.1080/00051144.2023.2218167","DOIUrl":"https://doi.org/10.1080/00051144.2023.2218167","url":null,"abstract":"Data used in big data applications are typically kept in decentralized computing resources in the real world, which has an impact on the design of artificial intelligence algorithms. When there are significantly more observations from one class than from another, the dataset is said to be imbalanced. Therefore, in this work, the study elaborates the model as SMOTE-SVM which resolves imbalance issues in sampling the data and improves overall accuracy to 94%. The model deploys K-nearest neighbours to compute the difference between samples and to balance the samples, it computes the kernel space. Further, to optimize the classification, GWO optimizer merges with SMOTE-SVM to achieve enhanced performance. GWO (Grey Wolf Optimizer) induces greedy selection to perform optimization among classification. It is important to remember that grey wolves have a flexible social structure that might change the hierarchy. As the mobilization continues, the grey wolves are reconstructed with the distance between them and their prey, or more specifically, in accordance with the resultant value of the fitness. In addition, to prove the efficiency, the following performance metrics are measured-Overall Accuracy, Classification Accuracy, AUC and ROC.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42852778","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

Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier 基于代价敏感的堆叠集成分类器的网格稳定性评估与分类

4区计算机科学

Automatika Pub Date : 2023-06-06 DOI: 10.1080/00051144.2023.2218164

Karthikeyan Ramasamy, Arivoli Sundaramurthy, Durgadevi Velusamy

{"title":"Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier","authors":"Karthikeyan Ramasamy, Arivoli Sundaramurthy, Durgadevi Velusamy","doi":"10.1080/00051144.2023.2218164","DOIUrl":"https://doi.org/10.1080/00051144.2023.2218164","url":null,"abstract":"Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135557660","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