scholarly journals Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

2019 ◽  
Vol 15 (2) ◽  
pp. 1009
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Multiple Regression ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Average Error ◽  
Listwise Deletion ◽  
Squared Error ◽  
Analysis Technique ◽  
The Future ◽  
History Of

<span>Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.</span>

scholarly journals Proposed agorithm for regression-based prediction with bulk noise

2020 ◽  
Vol 17 (1) ◽  
pp. 543
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Mean Squared Error ◽  
Cost Effective ◽  
Absolute Error ◽  
Original Data ◽  
Local Solution ◽  
Average Error ◽  
Decomposition Approach ◽  
Squared Error ◽  
Cost Effective Method ◽  
Global Impacts

<span>The noise has incited an original data due to a network with an inferior SNR. In case of the bulk noise, the insightful content within the data is substantially squeezed.  A cost-effective method will challenge to quarantine the insights, so that information can be utilized more resourcefully.  To achieve this aim, it is essential to iron the bulk noise content out, and then calculate the analytics of the clean data. As noise is bulk so some statistical methodologies such as averaging or randomizing are employed. A prediction using the regression-based model with bulk noise for the experiment in practice is introduced. The decomposition approach to separate the insights is exploited. The proposed algorithm achieves a (local) solution at each computing step and selects the best solution in view of global impacts. The correlation coefficient, average error, absolute error and mean squared error are used to constitute the prediction. Results from MOA simulation will be compared to actual data in the succeeding time. The prediction with bulk noise using the proposed algorithm outperforms.</span>

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Comparison of ANFIS and FAHP-FGP methods for supplier selection

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 474-489 ◽  
Author(s):  
Moloud sadat Asgari ◽  
Abbas Abbasi ◽  
Moslem Alimohamadlou
Keyword(s):  
Performance Measures ◽  
Supplier Selection ◽  
Mean Squared Error ◽  
Selection Process ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Global Market ◽  
Content Type ◽  
Squared Error ◽  
Fuzzy Delphi

Purpose – In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers. Design/methodology/approach – ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2. Findings – The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures. Practical implications – The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment. Originality/value – This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

scholarly journals Software-based simulation for preprocedural assessment of braided stent sizing: a validation study

2019 ◽  
Vol 131 (5) ◽  
pp. 1423-1429 ◽  
Author(s):  
Krishna Chaitanya Joshi ◽  
Ignacio Larrabide ◽  
Ahmed Saied ◽  
Nada Elsaid ◽  
Hector Fernandez ◽  
...  
Keyword(s):  
Intracranial Aneurysms ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Study Data ◽  
Optimal Placement ◽  
Squared Error ◽  
Single Center Study ◽  
Measured Length ◽  
The Right ◽  
Ruptured Intracranial Aneurysms

OBJECTIVEThe authors sought to validate the use of a software-based simulation for preassessment of braided self-expanding stents in the treatment of wide-necked intracranial aneurysms.METHODSThis was a retrospective, observational, single-center study of 13 unruptured and ruptured intracranial aneurysms treated with braided self-expanding stents. Pre- and postprocedural angiographic studies were analyzed. ANKYRAS software was used to compare the following 3 variables: the manufacturer-given nominal length (NL), software-calculated simulated length (SL), and the actual measured length (ML) of the stent. Appropriate statistical methods were used to draw correlations among the 3 lengths.RESULTSIn this study, data obtained in 13 patients treated with braided self-expanding stents were analyzed. Data for the 3 lengths were collected for all patients. Error discrepancy was calculated by mean squared error (NL to ML −22.2; SL to ML −6.14, p < 0.05), mean absolute error (NL to ML 3.88; SL to ML −1.84, p < 0.05), and mean error (NL to ML −3.81; SL to ML −1.22, p < 0.05).CONCLUSIONSThe ML was usually less than the NL given by the manufacturer, indicating significant change in length in most cases. Computational software-based simulation for preassessment of the braided self-expanding stents is a safe and effective way for accurately calculating the change in length to aid in choosing the right-sized stent for optimal placement in complex intracranial vasculature.

scholarly journals Multiscale Aggregate Networks with Dense Connections for Crowd Counting

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pengfei Li ◽  
Min Zhang ◽  
Jian Wan ◽  
Ming Jiang
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Image Features ◽  
Convolutional Network ◽  
Crowd Counting ◽  
Squared Error ◽  
Crowd Density ◽  
Density Maps ◽  
Density Map ◽  
Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

Anticipation of Demand in Supply Chains

2019 ◽  
pp. 1-45
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Moving Average ◽  
Demand Management ◽  
Inventory Level ◽  
Squared Error ◽  
Interfirm Collaboration ◽  
Overall Performance

The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

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