Small Number Of Input Variables Research Articles

Machine learning for detecting fake accounts and genetic algorithm-based feature selection

Abstract People rely extensively on online social networks (OSNs) in Africa, which aroused cyber attackers’ attention for various nefarious actions. This global trend has not spared African online communities, where the proliferation of OSNs has provided new opportunities and challenges. In Africa, as in many other regions, a burgeoning black-market industry has emerged, specializing in the creation and sale of fake accounts to serve various purposes, both malicious and deceptive. This paper aims to build a set of machine-learning models through feature selection algorithms to predict the fake account, increase performance, and reduce costs. The suggested approach is based on input data made up of features that describe the profiles being investigated. Our findings offer a thorough comparison of various algorithms. Furthermore, compared to machine learning without feature selection and Boruta, machine learning employing the suggested genetic algorithm-based feature selection offers a clear runtime advantage. The final prediction model achieves AUC values between 90% and 99.6%. The findings showed that the model based on the features chosen by the GA algorithm provides a reasonable prediction quality with a small number of input variables, less than 31% of the entire feature space, and therefore permits the accurate separation of fake from real users. Our results demonstrate exceptional predictive accuracy with a significant reduction in input variables using the genetic algorithm, reaffirming the effectiveness of our approach.

True sparse PCA for reducing the number of essential sensors in virtual metrology

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance.

Unlocking the power of machine learning for Earth system modeling: A game-changing breakthrough.

Unlocking the power of machine learning for Earth system modeling: A game-changing breakthrough.

Prediction of Wave Transmission Characteristics of Low Crested Structures Using Artificial Neural Network

<i>Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10<sup>-3</sup>, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.</i>

A new multiobjective optimization with elliptical constraints approach for nonlinear models implemented in a stainless steel cladding process

This paper proposes a new multiobjective optimization with elliptical constraints approach for nonlinear models implemented in a cladding process of ABNT 1020 carbon steel plate using austenitic ABNT 316L stainless steel cored wire. Stainless steel stands out among the cladding materials as it allows obtaining surfaces with determined desirable characteristics from lower cost materials. This kind of process may be controlled by a relatively small number of input variables, i.e., the wire feed rate (WF), voltage (V), welding speed (WS), and the distance from the contact tip to the workpiece (N). Besides that, many outputs can be evaluated and simultaneously optimized. In the present paper, dilution (D), yield (Y), convexity index (CI), and penetration index (PI) were investigated. In order to consider the problem’s multivariate nature, techniques such as factor analysis and Bonferroni’s multivariate intervals were applied combined with elliptical constraints. The response variables were mathematically modeled using Poisson regression, and the obtained results were satisfactory since accurate models were achieved. The normal boundary intersection (NBI) method produced a set of viable configurations for the input variables that allows the experimenter to encounter the best system setup regarding the importance level of each response. Feasible and non-dominated solutions were found which means that the best possible scenario considering all the constraints was reached.

Simultaneous identification of groundwater pollution source location and release concentration using Artificial Neural Network

Groundwater pollution source identification problem plays an important role in designing the groundwater remediation measures. This study aims at identifying the groundwater pollutant source location and its strength given a set of concentration breakthrough curves at different locations. Feed forward three layered artificial neural network (ANN) has been used to identify these two source parameters: location, i.e., the distance of source from the observation point and strength, i.e., the release concentration. A simplified ANN architecture is achieved by presenting the available concentration breakthrough curves in a specific manner as input to the model. The database of 1750 patterns for training and testing of the model is generated employing analytical solution of one dimensional steady flow and transient contaminant transport in a homogeneous aquifer. The performance of the ANN model was evaluated using standard statistical methods. A network with architecture 22-16-2 was found to be optimum and resulted in a normalized root mean square error (NRMSE) of 0.014 for identifying the source location and NRMSE of 0.044 for identifying the source release concentration. The results show that ANN can be a very efficient tool for locating pollution sources and estimating the release concentration at source. A good ANN model performance was obtained even with a simple architecture and with a small number of input variables.

EARLY Heart Disease Prediction with Minimal Attributes using Machine Learning

ABSTRACT- Heart Disease is one of major problem in our medical science and also in society. Largest-ever study on death shows that Heart Diseases have become Number one killer disease in the world. About 25 percent of deaths in the age group of 25 to 69 years occur due to heart diseases. In Our medical society all doctors are not equally skilled and experienced so every doctor has different opinion about Patients but still cases are reported of wrong diagnosis and treatment. There is need an intelligent system that predict about heart disease more accurately than doctors. In this paper we developed an intelligent Model using Neural Network that predict early Heart Disease base on minimum but major attributes of patient’s health with accuracy 90%. Finally, we got Impressive Results with small number of input variables of patients and the comparative study of the different Machine learning algorithms and tools.

Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach

Human joint moment plays an important role in quantitative rehabilitation assessment and exoskeleton robot control. However, the existing moment prediction methods require kinematic and kinetic data of human body as input, and the measurement of them needs special equipment, which makes them unable to be used in an unconstrained environment. According to the situation, this paper develops a novel method where a small number of input variables selected by Elastic Net are used as the input of artificial neural network (ANN) to predict joint moments, which makes the prediction in daily life possible. The method is tested on the experimental data collected from eight healthy subjects that are running on a treadmill at a speed of 2, 3, 4, and 5 m/s, respectively. Taking the right lower limb’s 10 electromyography (EMG) and 5 joints angle data as candidate variable sets, Elastic Net is used to obtain the variable coefficients of the right lower limb’s four joint moments. The inputs of the ANN determined by the variable coefficients are used to train and predict the joint moments. Prediction accuracy is evaluated by using the normalized root-mean-square error (NRMSE %) and cross correlation coefficient ( $\rho$ ) between the predicted joint moment and multi-body dynamics moment. Results of our study suggest that the method can accurately predict joint moment (NRMSE $\rho >0.9633$ ) with only 5–6 EMG signals. In conclusion, this method can effectively reduce the input variables while keeping a certain precision, which makes the joint moment prediction simple and out of equipment limitation. This method may facilitate the researches on real-time gait analysis and exoskeleton robot control in motor rehabilitation.

Is energy performance too taxing?

PurposeBuildings contribute significantly to CO2 production. They are also subject to considerable taxation based on value. Analysis shows that while similar attributes contribute to both value and CO2 production, there is only a loose relationship between the two. If we wish to use taxation to affect policy change (drive energy efficiency behaviour), we are unlikely to achieve this using only the current tax base (value), or by increasing the tax take off this current tax base (unlike extra taxation of cigarettes to discourage smoking, for example). Taxation of buildings on the basis of energy efficiency is hampered by the lack of current evidence of performance. This paper aims to model the now-obligatory (at sale or letting) energy performance certificate (EPC) data to derive an acceptable appraisal model (marked to market, being the EPC scores) and deploys this to the entire population of properties. This provides an alternative tax base with which to model the effects of a tax base switch to energy efficiency and to understand the tax incidence effects of such a policy.Design/methodology/approachThe research uses a multiplicative hedonic approach to model energy efficiency utilising EPC holding properties in a UK jurisdiction [Northern Ireland (NI)] as the sample. This model is then used to estimate discrete energy assessments for each property in the wider population, using attributes held in the domestic rating (property tax) database for NI (700,000+ properties). This produces a robust estimate of the EPC for every property in its current condition and its cost-effective improved condition. This energy assessment based tax base is further used to estimate a new millage rate and property tax bill (green property tax) which is compared against the existing property tax based on value to allow tax incidence changes to be analysed.FindingsThe findings show that such a policy would significantly redistribute the tax burden and would have a variety of expected and some unexpected effects. The results indicate that while assessing the energy performance of houses can be a complex process involving many parameters, much of the explanatory power can be achieved via a relatively small number of input variables, often already held by property tax jurisdictions. This offers the opportunity for useful housing stock modelling – such as the savings possible from power switching. The research also identifies that whilst urban areas display the expected “heat island” effect in terms of energy consumption, urban properties are on average more efficient than suburban/rural properties. This facilitates spatial targeting of policy messages and initiatives.Research limitations/implicationsAnalogous with other studies, data deficiencies introduce the risk of omitted variable bias. Modelling of the energy efficiency in the sample is limited to property attributes that are available for the wider population of properties. While this limits the modelling exercise, it is a perennial issue facing mass appraisal worldwide (where knowledge of the transacted sample attributes generally exceeds knowledge of the unsold properties). That said, the research demonstrates the benefits of sharing data and improving knowledge of the housing stock, as taxation databases would be stronger, augmented with EPC-derived property attributes for example.Originality/valueThe EPC lead in time for wide residential coverage is likely to be considerable. The paper contributes to emerging literature and policy debate surrounding the effect, performance measurement and implementation of energy efficiency certification, through a greater understanding of the sectorial and geographical dispersion of energy efficiency. It provides high level research to help guide policy and decision-making, identifying key locales where there is more of a physical problem and locations where there is more to gain in terms of targeting energy improvement and/or encouraging behavioural change. The paper also allows a glimpse of the implications of a change towards a taxation regime based on energy efficiency, which contributes to the debate surrounding the “greening” of property based taxes.

Statistical model of tyre-road noise for thin layer surfacing

Noise produced from the tyre-road surface interface is one of the most important contributions to the overall traffic noise and there is an increasing requirement for predicting the tyre-road noise levels prior to road construction in the Netherlands. In practice, a model with a simple structure as well as a high accuracy is applicable in road engineering. Also, material properties are preferred to be used as input variables of the prediction model, which will facilitate the pavement design. Based on these considerations, models are developed for evaluating the tyre-road noise from the asphalt mixture compositions and road surface characteristics. They are statistical models developed from the measurements on thin layer surfacings in the Netherlands. Different regression methods, model types and input variable combinations are taken into account. The selection of the model is due to the fitness of the prediction and validation by using measurement data from in service road sections. Two models, which evaluate the tyre-road noise level from the surface characteristics and from material properties, respectively, are finally selected. By using these models, only a small number of input variables are required and reliable predictions can be provided. The models achieved in this study can be used for predicting the tyre-road noise generation in road engineering and investigating the influence of surface characteristics and material properties on tyre-road noise levels.

Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain.

Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (-0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R(2) between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R(2) between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.

Elastic net with Monte Carlo sampling for data-based modeling in biopharmaceutical manufacturing facilities

Biopharmaceutical manufacturing involves multiple process steps that can be challenging to model. Oftentimes, operating conditions are studied in bench-scale experiments and then fixed to specific values during full-scale operations. This procedure limits the opportunity to tune process variables to correct for the effects of disturbances. Generating process models has the potential to increase the flexibility and controllability of the biomanufacturing processes. This article proposes a statistical modeling methodology to predict the outputs of biopharmaceutical operations. This methodology addresses two important challenging characteristics typical of data collected in the biopharmaceutical industry: limited data availability and data heterogeneity. Motivated by the final aim of control, regularization methods, specifically the elastic net, are combined with sampling techniques similar to the bootstrap to develop mathematical models that use only a small number of input variables. This methodology is evaluated on an antibody manufacturing dataset.

Personality Classification of Consumers: A Comparison of Variables, Standardization and Clustering Methods

AbstractThe use of personality trait measurement is increasing in sensory evaluation for linking certain variables (i.e., consumption behavior and product preferences) to particular attributes. For this study, 976 consumers rated agreement on 44 statements from the Big Five Inventory using a 5‐point Likert‐type scale. Data handling methods for personality segmentation were compared: (1) the original 44 variables versus the five computed personality variables; (2) standardization versus nonstandardization of data; and (3) k‐means versus Ward's hierarchical clustering method used with principal component analysis.Results indicate using the five computed variables in mapping gave higher percentages of explained variability because of the small number of input variables. However, maps created from the 44 individual variables illustrated that participants were distributed throughout and separated visually into groups. Standardization of the data set did not affect mapping or classification. k‐means and Ward's clustering methods provided different classification results within the same data set.Results suggest that when using the Big Five personality traits measurement, the original 44 unstandardized variables and k‐means clustering should be used for obtaining consumer segmentation because this captures the greater variability inherent in the 44 variable tests and easily separates consumers into personality groups.Practical ApplicationsPrevious research has demonstrated that demographic and economic information provides insufficient explanation for consumer preference and other subjective responses. An application of personality research would aid researchers in understanding psychological factors that influence subjective responses. The study suggests that when using the Big Five personality traits tool, taking time to compute five personality traits is not needed and, in fact, detracts from grouping consumers into clusters. In addition, there is no need to standardize data during data preparation. However, selecting and using an appropriate clustering method for placing consumers into personality groups does impact the outcome. Based on this research k‐means clustering is recommended. The personality classification could be applied in consumer segmentation for a better understanding of consumers.

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Fuzzy-CCM: A context-sensitive approach to fuzzy modeling

The objective of this work is to present a fuzzy modeling method based on the conditional fuzzy clustering algorithm - the Fuzzy-CCM (Fuzzy Conditional Clustering Modeling) method. The balance between interpretability and accuracy of fuzzy rules is addressed by means of the definition of contexts formed with a small number of input variables and the generation of clusters conditioned by the context defined. The rules are generated in a different format which have linguistic variables with their values as well as groups. Some experiments have been run using different domains in order to validate the proposed approach and to compare the results with the ones obtained with the WangM and FCMeans methods. The advantages of the method, the experiments and the results obtained are discussed.

Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines

A new calibration method that accurately predicts the Research Octane Number (RON) values of gasoline fractions, based on their infrared spectra, is presented. This model combines Linear Predictive Coding (LPC) and multiple linear regression (MLR) as an integrated estimation technique. Spectral information from the 4800–3520 cm −1 range was initially encoded into Linear Predictive (LP) coefficients, which were used as predictor variables in the MLR model against RON values. The model was trained and tested on an extensive data set (384 gasoline samples) and found to ensure prediction accuracy of 0.3 RON Root Mean Squared Error (RMSE). The LPC technique was found to be efficient in capturing spectral features of the entire range, related to the RON characteristics of the gasoline samples, without the need of any pretreatment on the experimental raw data. The small number of input variables in the regression model ensures a robust, easy-to-use and high accuracy prediction model.

Predicting business failure: an application of multicriteria decision aid techniques in the case of small UK manufacturing firms

Small firms play an important role in most modern economies. Nevertheless, they are not without their problems. Therefore, the development of business failure prediction models for small firms is quite important. In the present study we employ three multicriteria decision aid (MCDA) techniques for the development of failure prediction models for small UK firms. The MCDA models are developed using a ten-fold cross validation and benchmarked against models developed with discriminant analysis and logit analysis. All models achieve satisfactory classification accuracies while including only a small number of input variables, hence avoiding the commonly encountered problem of data availability when dealing with small firms. Also we examine two non-financial variables – the age of the firm and auditors' opinion – but the classification results are only marginally affected.

Development and validation of a leaf wetness duration model using a fuzzy logic system

A model to estimate leaf wetness duration (LWD) based on fuzzy logic was developed and validated using hourly weather measurements at 15 sites in the midwestern U.S. from 1997–1999. The Fuzzy model, which required relatively few input variables and simplified calculations compared with physical models, was comparable to sensor measurements in overall accuracy of LWD estimation (<1 h/day error). The Fuzzy model was also more portable than the CART/SLD/Wind model, an empirical model that used the same weather variables, in that the Fuzzy model classified presence or absence of wetness more accurately than the CART/SLD/Wind model at most sites. This suggests that incorporating energy balance principles and empirical computation methods in a fuzzy logic system makes it possible to accurately estimate LWD with a relatively small number of input variables. The accuracy of the Fuzzy model may be improved using correction factors when additional relevant inputs, e.g., solar radiation, become available. Therefore, the Fuzzy model may merit further study to verify this adaptability.

Neural network modelling of coastal algal blooms

An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982–2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996–2000). Algal biomass is represented as chlorophyll- a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1–2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available.

Influential input classification in probabilistic multimedia models

Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the variance (uncertainty and/or variability) associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions one should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the spread of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.