Regularization Factor Research Articles

Subspace learning using structure learning and non-convex regularization: Hybrid technique with mushroom reproduction optimization in gene selection

Gene selection as a problem with high dimensions has drawn considerable attention in machine learning and computational biology over the past decade. In the field of gene selection in cancer datasets, different types of feature selection techniques in terms of strategy (filter, wrapper and embedded) and label information (supervised, unsupervised, and semi-supervised) have been developed. However, using hybrid feature selection can still improve the performance. In this paper, we propose a hybrid feature selection based on filter and wrapper strategies. In the filter-phase, we develop an unsupervised features selection based on non-convex regularized non-negative matrix factorization and structure learning, which we deem NCNMFSL. In the wrapper-phase, for the first time, mushroom reproduction optimization (MRO) is leveraged to obtain the most informative features subset. In this hybrid feature selection method, irrelevant features are filtered-out through NCNMFSL, and most discriminative features are selected by MRO. To show the effectiveness and proficiency of the proposed method, numerical experiments are conducted on Breast, Heart, Colon, Leukemia, Prostate, Tox-171 and GLI-85 benchmark datasets. SVM and decision tree classifiers are leveraged to analyze proposed technique and top accuracy are 0.97, 0.84, 0.98, 0.95, 0.98, 0.87 and 0.85 for Breast, Heart, Colon, Leukemia, Prostate, Tox-171 and GLI-85, respectively. The computational results show the effectiveness of the proposed method in comparison with state-of-art feature selection techniques.

Soft sensing of calcination zone temperature of lime rotary kiln based on principal component analysis and stochastic configuration networks

The combustion state of the lime rotary kiln is mainly reflected in the temperature of the calcination zone. However, due to the high temperature in the calcination zone during the production process, it is not possible to directly measure using instruments. This paper proposes a soft sensing strategy to address the issue of difficulty in measuring the temperature of the calcination zone of a lime rotary kiln. Firstly, due to significant disturbances and coupling of production process variables, principal component analysis is used to reduce the input dimension and computational complexity of the data, thereby improving the efficiency of the soft sensing model. Then, stochastic configuration networks with good predictive performance is used to model and predict the principal components. Meanwhile, an improved Hammersley-based Salp swarm algorithm is proposed to optimize the key parameter regularization factor of the stochastic configuration networks. Finally, the validity of the soft sensing model is verified by the actual collected production data of the lime rotary kiln. Multiple evaluation indicators and comparison results indicate that the proposed soft sensing model for the calcination zone temperature of lime rotary kilns has higher prediction accuracy and modeling quality compared to other models.

Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems

Fault section diagnosis (FSD) is extremely critical to ensure the safe and stable operation of power systems. Neural networks have become popular increasingly in solving this problem and the existing applied ones rely primarily on the traditional BP and RBF neural networks. However, their generalization is weak when diagnosing large-scale power systems. To address this issue, this study suggests a divisional FSD method based on structure adaptive hierarchical extreme learning machines (HELM). To construct an optimal HELM-based sub-module diagnosis model for each subsystem of a large-scale power system, differential evolution is applied to optimize both the number of hidden layer neurons and the regularization factor of HELM simultaneously. The optimal HELM is responsible for completing both the diagnosis of internal power sections of the subsystem and the interim diagnosis of tie lines of adjacent subsystems. Based on the interim diagnosis results, Choquet fuzzy integral is used to fuse them to obtain the final results for the tie lines. Simulation results indicate that the differential evolution algorithm is effective in finding more accurate and compact HELMs with better approximation ability and generalization performance. The proposed FSD method has higher accuracy and fault tolerance to identify various complex fault cases.

Determinants of manifestations of the epidemic process of epidemic hemorrhagic fever in Primorsky Krai

BACKGROUND: The contribution of random and regular factors in the process of epidemic hemorrhagic fever is unknown, which necessitates the search for the reasons for the formation of severe and fatal cases.
 AIM: Based on the incidence of epidemic hemorrhagic fever in Primorsky Krai over a long period, we aimed to establish the role of the main determinants of the epidemic process and to demonstrate the causality of local manifestations in epidemic foci in patients with epidemic hemorrhagic fever.
 MATERIALS AND METHODS: In this comprehensive descriptive epidemiological and clinical study, we undertook the epidemiological analysis of the incidence of epidemic hemorrhagic fever and data from examinations of the foci of infection. We performed a clinical observation (case study) of a case of epidemic hemorrhagic fever with a fatal outcome at the Regional Clinical Infectious Diseases Hospital in March 2022. The object of the study is statistical data on officially registered cases of epidemic hemorrhagic fever in Primorsky Krai from 1995 to 2021.
 RESULTS: The proportion of random factors in the epidemic process of epidemic hemorrhagic fever in the period under review was 28.7% of the incidence, which formed deviations from the typical curve, but did not participate in the formation of cyclicity in the long-term dynamics. Variable factors that formed cyclicity and random deviations together determined 53.4% of the incidence. They were fully related to seasonal morbidity (67.8%). Moreover, the remaining 14.4% of seasonality was formed by constant factors for each month. Factors constant for all months of the year form a year-round form, that is, 32.2% of the incidence.
 In the clinical and epidemiological observation of a lethal case of epidemic hemorrhagic fever, infection was noted under conditions of dust formation, such as when restoring order in a closed room at a negative temperature (sweeping) and high titers of specific antibodies resulting from repeated encounters with the pathogen in residential and occupational types of morbidity and the accompanying illnesses.
 CONCLUSIONS: The study of the incidence of epidemic hemorrhagic fever demonstrated a high resolution of epidemiological analysis by using a binary method in zoonoses. The patterns and features of human morbidity were controlled by natural and social factors more strictly than by biological ones. The determinants of the socio-ecological system in epidemic hemorrhagic fever in Primorsky Krai make it possible to specify the tactics and strategy for the implementation of epidemiological surveillance.

Frustratingly Easy Truth Discovery

Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we consider an extremely simple heuristic for estimating workers' competence using average proximity to other workers. We prove that this estimates well the actual competence level and enables separating high and low quality workers in a wide spectrum of domains and statistical models. Under Gaussian noise, this simple estimate is the unique solution to the MLE with a constant regularization factor. Finally, weighing workers according to their average proximity in a crowdsourcing setting, results in substantial improvement over unweighted aggregation and other truth discovery algorithms in practice.

Transmit Power Optimization and Precoding Design in Multiuser Satellite MIMO Downlink With SINR Constraints

In this paper, a multi-user (MU) satellite Multiple-Input Multiple-Output (MIMO) downlink including a multi-beam GEO satellite and multiple terrestrial users is considered. In order to meet the signal to interference plus noise ratio (SINR) requirements of different users while minimizing the satellite transmit power, the onboard precoding problem is investigated. Zero forcing (ZF) and regularized zero-forcing (RZF) precoding are first adopted and it is found that they can satisfy the SINR constraints by multiplying by a power allocation matrix. The required power allocation matrix is divided into two types: equal power factor schemes and unequal power factor schemes, and they are derived separately, where Karush-Kuhn-Tucker (KKT) conditions are used in RZF unequal power factor scheme and a heuristic algorithm is proposed to optimize the regularization factor in RZF schemes to further reduce the transmit power. Then, a semi-definite programming beamforming (SDPBF) is proposed to jointly optimize precoding and power allocation matrices. The closed-form expressions of the transmit power of the above five precoding schemes are derived and their performance is compared in the simulation part. The research results show that under the same conditions, the performance of RZF is better than that of ZF, the unequal power factor scheme is better than the equal power factor scheme, and SDPBF has the best performance. Besides, it is also found that in addition to changing the precoding method, activating more beams will further reduce the transmit power.

Measuring direction of arrival in cognitive radio systems using variable step adaptive learning for spectrum sensing

In wireless communications cognitive radios are rapidly growing technology and it is used in medical telemetry applications to provide treatment to remote located patients on time. Generally, interferences are occurred due to a shortage of frequency range and spectrums. To avoid these interferences new technologies are developed with cognitive radios. In the received signal there are some noisy signals are occurred due to signal estimation is not done properly. For estimating input signal primary user and secondary user signals direction of arrival is taken into consideration. To improve further output and to remove noise signals of received signal proposed an adaptive learning methodology. In this paper a regularization based variable step adaptive learning algorithm (VSALA) is developed for avoiding minimum disturbance constraint functions in cost function. Further regularization factor and step size combination results a faster convergence when compared to conventional normalized adaptive algorithm (NALA). The proposed learning methodology is analysed for different threshold values like 0.1, 0.5 and 1.0 for estimating the direction of arrival (DOA) and their corresponding beam forming patterns are shown.

Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices

Existing factor models struggle to model the covariance matrix for a large number of stocks and factors. Therefore, we introduce a new covariance matrix estimator that first shrinks the factor model coefficients and then applies nonlinear shrinkage to the residuals and factors. The estimator blends a regularized factor structure with conditional heteroskedasticity of residuals and factors and displays superior all-around performance against various competitors. We show that for the proposed double-shrinkage estimator, it is enough to use only the market factor or the most important latent factor(s). Thus there is no need for laboriously taking into account the factor zoo.

Gradient filtering regularization for 3-D MT inversion based on unstructured tetrahedral discretization

SUMMARY We propose a novel smoothing regularization scheme for 3-D magnetotelluric (MT) inversion based on unstructured tetrahedral discretization. Different from conventional methods that explicitly add smoothing constraints to model parameters, we choose to do the gradient filtering to smooth the model updates in an implicit way. By transforming the model into a constraint domain, the gradient of the objective function for the parameters in the new domain can be taken as a product of transpose of inverse transformation operator and the conventional gradient. Since the transpose of inverse transformation is designed to be an inverse distance interpolation operator for each tetrahedron, the data fitting term in the gradient can be smoothed in a filtering-like process. We compare our new strategy with the conventional explicit smoothing ones by testing on synthetic data for different noise levels, initial models and regularization factors. The numerical results show that suffering from the unequal volume and random centroid location of adjacent tetrahedrons, the inversion results of conventional methods often demonstrate scattered structures in slices. In contrast, our new method recovers the model in a smooth way and the convergence speed is largely improved. Finally, we adopt the USArray data for further testing and find that comparing to conventional inversion methods, our new strategy can provide more reliable underground structures with better data fitting.

A cross-sectional study on the prevalence of cardiovascular risk factors in police personnel of Karimnagar mandal

Abstract Introduction: Many studies showed that police officers complained about physical and mental health issues due to job stress. These work stresses lead to adopting unhealthy lifestyles and habits including the harmful use of alcohol and tobacco use which leads to high blood pressure, heart attacks, and other NCDs. Methods: This cross-sectional study included all male police personnel of all cadres from nine police stations of Karimnagar Mandal. WHO STEPS NCD survey questionnaires were used to collect basic soci-demographic information, clinical history, stress, and anthropometry for measurements of CVD risk factors. Result: 253 police personnel with a mean age of 41.09 years enrolled from all cadres. 152 (60%) of police personnel had multiple cardiovascular risk factors. Hypercholesterolemia (37.2%) followed by a sedentary lifestyle (30.8%), and diabetes mellitus (14.6%) were important findings in this study. This study has also revealed an important link between preceding conditions for developing cardiovascular diseases such as pre-hypertension in 42.7% and impaired fasting glucose level in 25.7% of police personnel. Half of the study police personnel had a family history of NCDs and it was not statistically significant with service experience. There was a strong association between years of experience and tobacco and alcohol use. Organizational and operational stress levels increased with the increase in years of experience in policing (p-value<0. 01). Conclusion: Smoking and alcohol consumption were more prevalent among Police personnel which need to be controlled. Physical inactivity was another important area. Their attitude towards controlling the risk factors and practicing healthy behavior to safeguard from cardiovascular diseases has to be strengthened. It can be done by conducting regular awareness camps, training sessions, and regular cardiac risk factor evaluations for all police personnel.

Birds as Indicators of Active Restoration in the Western Ghats

The birds present in an area help in the up-grading the normal biodiversity. They portray the regular environmental factors of that particular area. They have always been considered as natural indicators of environmental change. They help in seed scattering, which aids in the germination of new trees that assist us with looking into the botanical variety of a specific region. Assuming we change the infertile grounds into forested regions by establishing trees, the birds are the initial ones to arrive at and occupy the area. The animals, especially the birds, are threatened by the changing environmental conditions. Moreover, they are the initial ones to get impacted. The birds select their habitats depending on their size, how they build their homes, and food propensities. Due to land use by humans, the habitat occupied by birds is subjected to fragmentation. The bird populations start getting isolated. In these isolated populations, many genetic changes take place that changes the wild gene pool into something different. This review paper deals with how effective birds are as indicators of restoration and the need to conserve the biodiversity of the Western Ghats.

Dual-graph regularized concept factorization for multi-view clustering

Matrix factorization is an important technology that obtains the latent representation of data by mining the potential structure of data. As two popular matrix factorization techniques, concept factorization (CF) and non-negative matrix factorization (NMF) have achieved excellent results in multi-view clustering tasks. Compared with multi-view NMF, multi-view CF not only removes the non-negative constraint but also utilizes the idea of the kernel to learn the latent representation of data. However, both of them ignore the local geometric structure in the nonlinear low-dimensional manifold. Furthermore, most of the existing CF-based methods are designed for single-view tasks, which cannot be directly applied to multi-view clustering tasks. To tackle the above shortcomings, we present a new multi-view clustering algorithm, called dual-graph regularized concept factorization for multi-view clustering (MVDGCF). Specifically, we first extend conventional single-view CF to a multi-view version, which can explore the complementary information of multi-view data more effectively. Then we develop a novel dual-graph regularization strategy, which can simultaneously capture the local structure information of the data space and feature space, respectively. Moreover, an adaptive weight vector is introduced to balance the importance of different views. Finally, extensive experiments are carried out on seven datasets. The results show that our method is superior to several popular multi-view clustering methods.

DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization

Automatic modulation classification (AMC) is an essential and indispensable topic in the development of cognitive radios. It is the cornerstone of adaptive modulation and demodulation capabilities to perceive and understand surrounding environments and make corresponding decisions. In this paper, we propose a priori regularization method in deep learning (DL-PR) for guiding loss optimization during model training process. The regularization factor designed by the combination of inter-class confrontation factor, global and dimensional divergence can help increase the inter-class distance and reduce the intra-class distance of samples. While preserving the original information of received signals as much as possible, it makes full use of the prior knowledge in the signal transmission process and ultimately helps deep learning models to be well generalized on signals with various signal-to-noise ratios (SNRs). As far as we know, this is the first attempt to regularize deep learning models based on SNR distribution of samples to improve AMC accuracy. Moreover, it can be proved that priori regularization can be interpreted as implicit data augmentation and model ensemble methods. By comparing with a series of state-of-the-art AMC methods and different regularization techniques on the public dataset RadioML 2016.10a, experimental results of multiple deep learning models illustrate the superiority of DL-PR, including CNN with accuracy of 62.6% and inference time of 0.82 ms per signal, LSTM with 61.8% and 0.87 ms, and hybrid CNN–LSTM with 64.2% and 0.94 ms. In practical applications, DL-PR can be also easily applied to complex environments due to its robustness to hyper-parameters and SNR estimation.

Experimental Study on the Aerodynamic Performance and Wave Energy Capture Efficiency of Square and Curved OWC Wave Energy Conversion Devices

To develop novel wave energy conversion devices (WECDs) with excellent performance, the relative wave height and pressure in the chambers of square and curved oscillating water column (OWC) WECDs were compared through physical model tests, and the effects of regular incident wave factors, the opening length, the opening width, and the chamber volume on the aerodynamic performance and wave energy capture efficiency (WECE) of the WECDs were investigated. The results indicated good chamber performance for both the square and curved OWC WECDs. As the incident wave height increased, the wave surface elevation, WECE, and pressure in the chamber all increased, while the relative wave height in the chamber decreased. When the opening length, width, and area of the WECD increased, both the relative wave height and chamber pressure increased. The relative wave height in the chamber increased with decreasing chamber volume; however, the chamber pressure and intrachamber WECE decreased with increasing chamber volume. It is recommended that, in actual engineering applications, the overall efficiency of the device be improved by increasing the opening length, width, area and volume of the air chamber.

Patients’ understanding of long-term cardiovascular risks and associated health-seeking behaviours after pre-eclampsia

ObjectiveThe lifelong risks of cardiovascular disease following hypertensive disorders of pregnancy are well described. Awareness of these risks and associated health-seeking behaviours among affected individuals remains unclear. We aimed to...

An Adaptive Sparse Regularization Method for Response Covariance-Based Structural Damage Detection

Structural damage detection is usually an ill-posed inverse problem due to the contamination of measurement noise and model error in structural health monitoring. To deal with the ill-posed damage detection problem, l2-regularization is widely used. However, l2-regularization tends to provide nonsparse solutions and distribute identified damage to many undamaged elements, potentially leading to false alarms. Therefore, an adaptive sparse regularization method is proposed, which considers spatially sparse damage as a prior constraint since structural damage often occurs in some locations with stiffness reduction at the sparse elements out of the large total number of elements in an entire structure. First, a response covariance-based convex cost function is established by incorporating an l1-regularized term and an adaptive regularization factor to formulate the sparse regularization-based damage detection problem. Then, optimal sensor placement is conducted to determine the optimal measurement locations where the acceleration responses are adopted for computing the response covariance-based damage index and cost function. Further, the predictor-corrector primal-dual path-following approach, an efficient and robust convex optimization algorithm, is applied to search for solutions to the damage detection problem. Finally, a comparison study with the Tikhonov regularization-based damage detection method is conducted to examine the performance of the proposed adaptive sparse regularization-based method by using an overhanging beam model subjected to different damage scenarios and noise levels. The numerical study demonstrates that the proposed method can effectively and accurately identify damage under multiple damage scenarios with various noise levels, and it outperforms the Tikhonov regularization-based method in terms of high accuracy and few false alarms. The analyses on time consumption, adaptiveness of the sparse regularization factor, model-error resistance, and sensor number influence are conducted for further discussions of the proposed method.

Factorisation of the complete graph into spanning regular factors

We enumerate factorisations of the complete graph into spanning regular graphs in several cases, including when the degrees of all the factors except for one or two are small. The resulting asymptotic behaviour is seen to generalise the number of regular graphs in a simple way. This leads us to conjecture a general formula when the number of factors is vanishing compared to the number of vertices. The intermediate results include the probability that a set of moderately sparse regular graphs are edge-disjoint when randomly relabelled.

An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression.

Ridge regression (RR) has been commonly used in machine learning, but is facing computational challenges in big data applications. To meet the challenges, this article develops a highly parallel new algorithm, i.e., an accelerated maximally split alternating direction method of multipliers (A-MS-ADMM), for a class of generalized RR (GRR) that allows different regularization factors for different regression coefficients. Linear convergence of the new algorithm along with its convergence ratio is established. Optimal parameters of the algorithm for the GRR with a particular set of regularization factors are derived, and a selection scheme of the algorithm parameters for the GRR with general regularization factors is also discussed. The new algorithm is then applied in the training of single-layer feedforward neural networks. Experimental results on performance validation on real-world benchmark datasets for regression and classification and comparisons with existing methods demonstrate the fast convergence, low computational complexity, and high parallelism of the new algorithm.

Assessment of vascular endothelial growth factor a serum level in pediatric hemophilic arthropathy

BackgroundChildren with hemophilia have serious, recurrent joint bleeding that causes disabilities. Regular factor VIII concentrate replacements have not yet completely stopped intra-articular subclinical hemorrhages and permanent joint damage. The prevention of joint damage requires the early detection and management of this hemorrhage. Neoangiogenesis is crucial in the development of synovitis after recurrent hemophilic joint bleeding. This study assessed the level of serum Vascular endothelial growth factor A (VEGF-A) as a vascular biomarker in children with hemophilia A to determine its possible role as a diagnostic biomarker for hemophilic arthropathy.ResultA case–control study with 30 male children who had hemophilia A and 30 healthy controls was conducted. Patients had significantly higher serum VEGF-A levels than the control group (specificity was 70.0% and sensitivity was 83.3%). Hemophilia severity and Hemophilia Joint Health Score had a significant positive correlation with VEGF-A.ConclusionChildren with hemophilia A had significantly higher levels of VEGF-A in their serum. Additionally, VEGF-A had a significant positive correlation with Hemophilia Joint Health Score as well as the severity of the disease. In children with hemophilia A, VEGF-A can be used as a marker for early hemophilic arthropathy detection.

Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%