Dual-manifold regularized regression models for feature selection based on hesitant fuzzy correlation

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

Neutrosophic QUALIFLEX based on neutrosophic hesitancy index for selecting a potential antivirus mask supplier over COVID-19 pandemic

In order to handle simultaneously the cardinal and ordinal information in decision-making process, QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very well-known decision-making approach. In this work, we extend the classical QUALIFLEX method to neutrosophic environment and develop a neutrosophic QUALIFLEX (N-QUALIFLEX) method that uses the newly defined distance-based comparison approach. It is highly effective in solving multi-criteria decision problems in which both ratings of alternatives on criteria and weights of criteria are single-valued neutrosophic numbers (SVNNs), and their aggregated values are single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). A neutrosophic hesitancy index (NHI) of a SVNHN is introduced based on degrees of the truth-membership, indeterminacy-membership and falsity-membership, which is used to measure the degree of hesitancy of SVNHN. Considering the NHIS of SVNHFNs, we propose a distance-based comparison approach to determine the magnitude of the SVNHFNs. Then, we apply the comparison approach to define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index that are steps of the developed N-QUALIFLEX. By taking all possible permutations of alternatives with respect to the level of concordance/discordance into account, we determine the order of alternatives in final decision. Finally, a practical example on antivirus mask selection over the COVID-19 pandemic is provided to present the effectiveness and applicability of the proposed method, and a comparative study is conducted to show the advantages of the proposed method over other existing methods.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Estimation of Scour Propagation Rates around Pipelines While Considering Simultaneous Effects of Waves and Currents Conditions

Seabed offshore pipelines are widely applied to carry fluid over long distances of the seafloor. The design of offshore pipelines is conducted to bear quite a few environmental loading circumstances in order to provide a well-guarded and reliable fluid transition. Fluid leakage and pipeline vibration due to a failure of the pipeline are the prime causes of accidental catastrophes. Scour phenomena occur around offshore pipelines due to currents and/or wave conditions, consequently causing the susceptibility to pipeline failure. Then, scouring propagation rates require to be studied in three dimensions, namely beneath and normal to the offshore pipeline and the longitudinal direction of itself. In this research, Artificial Intelligent (AI) models are used to derive new regression equations based on the laboratory data for the estimation of 3D scour propagation patterns while seafloor offshore pipelines are exposed to simultaneous impacts of currents and waves. In this way, chiefly based on the experimental investigations conducted by Cheng and colleagues, seven sets of dimensional parameters were given in terms of the Shields’ parameter due to currents and waves, the Keulegan–Carpenter number, the ratio of embedment depth to pipeline diameter, the ratio of orbital velocity to current velocity, and the wave/current angle of attack. Dimensionless parameters were used to provide regression-based equations to evaluate scour propagation rates in three dimensions. The performance of AI models was evaluated by various statistical measures. The model based on our proposed equations performed better than the reported models in the literature. Even more importantly, we indicated that our model inherently has a reliable physical consistency for variations of dimensionless parameters against the scour propagation patterns.

Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches

In our study, we examined the characteristics of nascent entrepreneurs using the 2021 Global Entrepreneurship Monitor national representative data in Hungary. We examined our topic based on Arenius and Minitti’s four-category theory framework. In our research, we examined system-level feature sets with four machine learning modeling algorithms: multivariate adaptive regression spline (MARS), support vector machine (SVM), random forest (RF), and AdaBoost. Our results show that each machine algorithm can predict nascent entrepreneurs with over 90% adaptive cruise control (ACC) accuracy. Furthermore, the adaptation of the categories of variables based on the theory of Arenius and Minitti provides an appropriate framework for obtaining reliable predictions. Based on our results, it can be concluded that perceptual factors have different importance and weight along the optimal models, and if we include further reliability measures in the model validation, we cannot pinpoint only one algorithm that can adequately identify nascent entrepreneurs. Accurate forecasting requires a careful and predictor-level analysis of the algorithms’ models, which also includes the systemic relationship between the affecting factors. An important but unexpected result of our study is that we identified that Hungarian NEs have very specific previous entrepreneurial and business ownership experience; thus, they can be defined not as a beginner but as a novice enterprise.

Scour Propagation Rates around Offshore Pipelines Exposed to Currents by Applying Data-Driven Models

Offshore pipelines are occasionally exposed to scouring processes; detrimental impacts on their safety are inevitable. The process of scouring propagation around offshore pipelines is naturally complex and is mainly due to currents and/or waves. There is a considerable demand for the safe design of offshore pipelines exposed to scouring phenomena. Therefore, scouring propagation patterns must be focused on. In the present research, machine learning (ML) models are applied to achieve equations for the prediction of the scouring propagation rate around pipelines due to currents. The approaching flow Froude number, the ratio of embedment depth to pipeline diameter, the Shields parameter, and the current angle of attack to the pipeline were considered the main dimensionless factors from the reliable literature. ML models were developed based on various setting parameters and optimization strategies coming from evolutionary and classification contents. Moreover, the explicit equations yielded from ML models were used to demonstrate how the proposed approaches are in harmony with experimental observations. The performance of ML models was assessed utilizing statistical benchmarks. The results revealed that the equations given by ML models provided reliable and physically consistent predictions of scouring propagation rates regarding their comparison with scouring tests.

Group decision making method with hesitant fuzzy preference relations based on additive consistency and consensus

To address the situation where the multi-criteria decision making (MCDM) has problems with hesitant fuzzy preference relations (HFPRs), this paper develops a group decision making method considering the additive consistency and consensus simultaneously. First, a new normalized method for HFPRs is developed to address the situation where the evaluation information has different number of elements. Second, for improving the unacceptable consistent HFPRs, an algorithm is designed to derive acceptable consistent HFPRs. The main characteristic of the design algorithm is that the values that need to be revised are identified first, and then design the local adjustment process. Third, an algorithm is developed to obtain a group of normalized HFPRs (NHFPRs), considering the additive consistency of HFPRs. Fourth, for improving the individual consistency and group consensus simultaneously, an algorithm is designed to obtain a group of HFPRs with acceptable consistency and consensus. Finally, the method of determining the decision makers’ weights and a procedure for MCDM problems with HFPRs are given. An illustrative example in conjunction with comparative analysis is used to demonstrate the proposed method which is feasible and efficient for practical MCDM problems.

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.