Advances in Data Science and Adaptive Analysis

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.

Riswan Efendi

Mustafa Mat Deris

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

使用模糊时间序列模型预测马来西亚-印度尼西亚的石油产量和消费量

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

Pawel Rzeszucinski

Michal Juraszek

James R. Ottewill

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

集成经验模态分解与稀疏度测量作为增强时间同步平均齿轮诊断能力的工具

In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.

Shu-Mei Guo

Jason Sheng-Hong Tsai

Chin-Yu Chen

Tzu-Cheng Yang

An Improved Empirical Mode Decomposition Based on Time Scale Allocation Method and the 2D Mode Mixing Phenomenon Judgement

基于时间尺度分配方法改进的经验模态分解及二维模态混叠现象判断

In this paper, we study a class of functions that exhibit properties expected from intrinsic mode functions. A type of an empirical instantaneous frequency, depending on the extrema scale, is introduced and its proximity to the classical analytic instantaneous frequency is discussed. We also obtain a sufficient condition for positiveness of the instantaneous frequency and introduce a method similar in nature to EMD but with an empirical frequency as guide in lieu of empirical envelopes. The method is illustrated in several numerical examples.

Vesselin Vatchev

Intrinsic Fourier Mode Functions

固有傅里叶模态函数

Inference methods are proposed for the bivariate mean function of a continuous stochastic process with a two-dimensional domain. Nonparametric bivariate estimation is facilitated by thresholded projection estimators. Estimators adapt to the sparsity of the bivariate function. Oracle inequality results are developed to describe the adaptive inference methods. The construction of nonparametric bivariate confidence bands is presented. Implementation results show the applicability of the methods in practice.

Andrada E. Ivanescu

Adaptive Inference for the Bivariate Mean Function in Functional Data

函数数据中二元均值函数的自适应推断

A. Jabarali

Jemima Wesley Williams

Effects of COVID-19 Pandemic on Adolescents based on Coping Behaviour - Statistical Perspective

基于应对行为视角的COVID-19大流行对青少年的影响——统计学观点

Atila Prates Correia

Adriano K. Suzuki

Paulo H. Ferreira

Marcio L. L. Viola

Construction of new bivariate semilinear copulas based on the Ali-Mikhail-Haq, Farlie-Gumbel-Morgenstern and Plackett families

基于Ali-Mikhail-Haq、Farlie-Gumbel-Morgenstern和Plackett族的新双变量半线性Copula的构建

Traffic prediction is challenging due to the stochastic nonlinear dependencies in spatiotemporal traffic characteristics. We introduce a Graph Convolutional Gated Recurrent Unit Network (GC-GRU-N) to capture the critical spatiotemporal dynamics. Using 15-min aggregated Seattle loop detector data, we recontextualize the prediction challenge across space and time. We benchmark our model against Historical Average, LSTM, and Transformers. While Transformers outperformed other models, our GC-GRU-N came in a close second with notably faster inference time — six times quicker than Transformers. We offer a comprehensive comparison of all models based on training and inference times, MAPE, MAE, and RMSE. Furthermore, we delve into the spatial and temporal characteristics of each model’s performance.

Maged Shoman

Armstrong Aboah

Abdulateef Daud

Yaw Adu-Gyamfi

Graph Convolutional Gated Recurrent Unit Network for Traffic Prediction Using Loop Detector Data

基于环形检测器数据的交通预测图卷积门控循环单元网络

Space institutions place humans in analog environments that closely match the target environment, such as the Moon or Mars, to study space habitation. Seven Mars analog missions consisting of habitation units with published designs were selected for analysis using graph data science methodologies to provide insight into how their designers solved the challenge of living in an isolated, constrained, and distant environment. Social Network Analysis (SNA) and Label Propagation Approach (LPA) are utilized to examine the relationships between adjacent functional areas of habitations. The paper presents the final documentation that will be used as a design framework to generate layouts for a habitation project.

Gökhan Dede

Assessment of Mars Analog Habitation Plans Using Network Analysis Methodologies

火星模拟居住计划评估：基于网络分析方法

More than 85% of women die from cervical cancer (CC) in developing countries, which is one of the major causes of premature mortality worldwide. Early diagnosis and treatment are essential to reducing cancer mortality as they lead to greater improvements and longer patient survival. CC is associated with several risk factors. The data set may contain redundant, irrelevant, and unreliable features, due to which we may get unreliable results in the process of classification. Feature selection techniques could be observed as a probable solution for this type of problem. In this study, a Novel Genetic-inspired Binary Firefly Algorithm with Random Forest (NGBFA-RF) is proposed as a solution for dimensionality reduction and to find a good set of features to be used in the process of classification. This study was based on the CC Risk Factors, which contain 32 risk factors and four dependent variables. The imbalance of data was alleviated by using the SMOTE data sampling technique. The proposed method’s main goal is to improve predictive accuracy with a small number of features, thereby reducing classification errors. The proposed novel algorithm based on the Firefly Algorithm with genetic operations has shown better results than the other existing models. The efficacy of the proposed novel algorithm has been assessed based on accuracy, recall, precision, [Formula: see text]-score, and AUC–ROC curve values. Results have exposed that a reduced feature set is helpful in getting a better accuracy of 98% with only five features in the classification with NGBFA-RF along with the hybrid ensemble classifier.

CH. Bhavani

A. Govardhan

A Novel Genetic-Inspired Binary Firefly Algorithm for Feature Selection in the Prediction of Cervical Cancer

一种新颖的遗传启发式二进制萤火虫算法用于宫颈癌预测中的特征选择

Abbreviation
Journal Impact	0.46
Quartiles(Global)	MATHEMATICS, INTERDISCIPLINARY APPLICATIONS(Q4)

Publication Information	Publisher: ，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: ，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 0.00%

Advances in Data Science and Adaptive Analysis

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