International Journal of Nonlinear Analysis and Applications最新文献

Generalized $p$-Laplacian systems with lower order terms 具有低阶项的广义p -拉普拉斯系统

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2021.20666.2191

Farah Balaadich, E. Azroul

引用次数: 0

Perturbation of wavelet frames on non-Archimedean fields 非阿基米德场上小波帧的摄动

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2021.21909.2302

Ishtaq Ahmad, Neyaz Ahmad

引用次数: 0

Solvability and numerical method for non-linear Volterra integral equations by using Petryshyn’s fixed point theorem 用Petryshyn不动点定理求解非线性Volterra积分方程的可解性及数值方法

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2021.22858.2422

A. Deep, Ashisha Kumar, Syed Abbas, M. Rabbani

引用次数: 2

Almost maximal, e-maximal and e-simple fuzzy submodules 几乎极大、e极大和e简单模糊子模

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5463

Maysoun A. Hamel, I. Hadi

引用次数: 0

Action of topological groupoid on topological space 拓扑群拟对拓扑空间的作用

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5459

T. Majeed

引用次数: 0

The existence of periodic solutions to doubly degenerate Allen-Cahn equation with Neumann boundary condition 具有Neumann边界条件的双简并Allen-Cahn方程周期解的存在性

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5505

Raad A. Hameed, Maan A. Rasheed, H. Mustafa, Faez N. Ghaffoori

引用次数: 2

An innovative and robust technique for human identification and authentication based on a secure clinical signals transmission 一种基于安全临床信号传输的创新和稳健的人体识别和认证技术

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5542

Baqer A Hakim, Ahmed Dheyaa Radhi, Fuqdan A. Al-Ibraheemi

{"title":"An innovative and robust technique for human identification and authentication based on a secure clinical signals transmission","authors":"Baqer A Hakim, Ahmed Dheyaa Radhi, Fuqdan A. Al-Ibraheemi","doi":"10.22075/IJNAA.2022.5542","DOIUrl":"https://doi.org/10.22075/IJNAA.2022.5542","url":null,"abstract":"EEG (Electroencephalogram) is brain waves measure. It is available test allowed to discover the brain functions over time. The brain troubles are evaluated by EEG. It is used to locate the activity in the brain during a seizure and to consider the patients who suffer from brain functionality problems. These troubles include tumors, coma, confusion and long-term difficulties (such as weakness associated with a stroke). The acquisition of EEG signals requires contact and liveliness and these signals are changes under stress that make so potentially unnecessary if it is acquired under menace. In this paper, an innovative and robust solution for this problem is introduced. To this end, the manner depends on models of various data compression models of information-theoretic plus the metrics symmetry related to Kolmogorov complexity. The proposed procedure compares two EEG segments and clusters the data into three groups: a corresponding record for each participant, a distinct person for each group, and self-participant. The technique was used to determine the database participant based on EEG signals. Using a distance measuring approach suggested in this scheme, a 1-NN classifier was constructed. Nearly every person in the underlying database could be accurately identified by the classifier with $96%$ accuracy","PeriodicalId":14240,"journal":{"name":"International Journal of Nonlinear Analysis and Applications","volume":"13 1","pages":"603-613"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68125259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis 脑电信号评估和分类的混合技术，为神经和大脑疾病的诊断迈出了一步

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5590

A. Abdulbaqi, M. T. Younis, Younus Tahreer Younus, Ahmed J. Obaid

{"title":"A hybrid technique for EEG signals evaluation and classification as a step towards to neurological and cerebral disorders diagnosis","authors":"A. Abdulbaqi, M. T. Younis, Younus Tahreer Younus, Ahmed J. Obaid","doi":"10.22075/IJNAA.2022.5590","DOIUrl":"https://doi.org/10.22075/IJNAA.2022.5590","url":null,"abstract":"Electroencephalography (EEG) signals are commonly used to identify and diagnose brain disorders. Each EEG normal waveform consists of the following waveforms: Gamma(γ) wave, Beta (β) wave, Alpha (α), Theta (θ), and Delta (δ). The term Neurological Diseases ” NurDis ” is used to describe a variety of conditions that affect the nervous Epilepsy, neuro infections (bacterial and viral), brain tumors, cerebrovascular diseases, Alzheimer’s disease, and various dementias are all examples of neurological disorders. Encephalitis is one of the illnesses that affects the brain. The EEG signals used in this paper were from the CHB-MIT Scalp EEG database. The discrete wavelet transform (DWT) was utilized to extract characteristics from the filtered EEG data. Finally, classifiers such as K Nearest Neighbor (KNN) and Support vector machine (SVM) were used to categorize the EEG signals into normal and pathological signal classes using all of the computed characteristics. In order to categorize the signal in a normal and anomalous group, the KNN and SVM classifiers are employed. For both classifiers, performance assessments (accuracy, sensitivity and specificity) are determined. KNN classifier accuracy is 71.88%, whereas SVM classifier accuracy is 81.23%. The sensitivity of KNN and SVM are 80.14% and 77.31%, respectively. The KNN classification specificity is 69.62% and the SVM classification specificity is 98%. Both classifiers performance is evaluated using the confusion matrix.","PeriodicalId":14240,"journal":{"name":"International Journal of Nonlinear Analysis and Applications","volume":"13 1","pages":"773-781"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68125558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A modification of the Cayley-Purser algorithm 对Cayley-Purser算法的修改

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5559

Sameerah Faris Khlebus, Rajaa K. Hasoun, B. T. Sabri

引用次数: 0

Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm 基于人工授粉算法的环境污染测量误差半参数回归函数估计

International Journal of Nonlinear Analysis and Applications Pub Date : 2022-01-01 DOI: 10.22075/IJNAA.2022.5744

O. E. Musa, Sabah Manfi Ridha

{"title":"Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm","authors":"O. E. Musa, Sabah Manfi Ridha","doi":"10.22075/IJNAA.2022.5744","DOIUrl":"https://doi.org/10.22075/IJNAA.2022.5744","url":null,"abstract":"Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett's method, and Durbin's method), The non-parametric model is estimated by using kernel smoothing (Nadaraya Watson), K-Nearest Neighbor smoothing and Median smoothing. The Flower Pollination algorithms were employed and structured in building the ecological model and estimating the semi-parametric regression function with measurement errors in the explanatory and dependent variables, then compare the models to choose the best model used in the environmental scope measurement errors, where the comparison between the models is done using the mean square error (MSE). These methods were applied to real data on environmental pollution/ air pollution in the city of Baghdad, and the most important conclusions that we reached when using statistical methods in estimating parameters and choosing the best model, we found that the Median-Durbin model is the best as it has less MSE, but when using flower The pollination algorithm showed that the Median-Wald model is the best because it has the lowest MSE, and when we compare the statistical methods with the FPA in selecting semi-parametric models, we notice the superiority of the FP algorithm in all methods and for all models.","PeriodicalId":14240,"journal":{"name":"International Journal of Nonlinear Analysis and Applications","volume":"13 1","pages":"1375-1389"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68126364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0