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Comparison of Neural Network Techniques and multi-linear regression to Predict Properties and Classify Pepper Seeds

JOURNAL OF FOOD PROCESS ENGINEERING 2025年第3期48卷

作者： Yildirim, Demet Cevher, Elcin Yesiloglu Black Sea Agr Res Inst Soil & Water Resources Dept Agr Irrigat & Land Reclamat Samsun Turkiye Univ Ondokuz Mayis Fac Agr Dept Agr Machinery & Technol Engn Samsun Turkiye

Pepper seed quality is determined using the mechanical and physical properties through artificial neural networks (ANNs) to enable accurate and timely agricultural planning. The objective of this study is to develop a model that provides simple, precise, rapid, and cost-effective predictions based on thousand-grain weight, porosity, and various classifications for pepper seeds. To achieve this, three different models-artificial neural networks (ANN), radial basis function (RBF), and multiple linear regression analysis (MLR) were employed to estimate thousand-grain weight and porosity. The best-selected model was then used to classify 12 different pepper seed varieties. This applied model's performance was evaluated using the determination coefficient (R2), the root mean square error (RMSE), the mean relative percentage absolute error (MRPE), and the mean square error (MSE). A comparison of the ANN model results indicated that the input parameters-width, length, thickness, and bulk density-provided the optimal prediction model concerning R2, RMSE, MRPE, and MSE. For the testing dataset, the ANN model achieved values ranging from 0.88 to 0.92 for R2, 0.276 to 0.016 for RMSE, 1.647 to 0.232 for MRPE, and 0.138-0.008 for MSE using 5-8-1 and 8-10-1 network structures, respectively. These selected models can be used as a neurocomputing-based approach to predict the mechanical and physical properties of pepper seeds, assisting in variety classification and genotype prediction.

关键词： best model multi-linear regression pepper seeds various and genotypes soft computing technique

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Simulating winter maintenance efforts: A multi-linear regression model

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COLD REGIONS SCIENCE AND TECHNOLOGY 2024年 227卷

作者： Mohammadi, Nafiseh Klein-Paste, Alex Lysbakken, Kai Rune NTNU Dept Civil & Environm Engn N-7034 Trondheim Norway SINTEF Community Infrastruct N-7034 Trondheim Norway

Winter Road Maintenance (WRM) ensures road mobility and safety by mitigating adverse weather conditions. Yet, it is costly and environmentally impactful. Balancing these expenses, impacts, and benefits is challenging. Simulating winter maintenance services offers a potential new tool to find this balance. In this paper, we analyze Norway's WRM of state roads during the 2021-2022 winter season and propose an effort model. This model forms the computational core of the simulation, predicting the number of plowing, salting, and plowing-salting operations at any given location over the road network. This is a multi-linear regression model based on the Gaussian/OLS method and comprises three sub-models, one for each of the aforementioned operations. The key explanatory variables are: 1) level of service (LOS), 2) road width, 3) height above mean sea level, 4) Average Annual Daily Traffic (AADT), 5) snowfall duration, 6) snow depth, 7) number of snow days (fallen snow and drifting snow), 8) number of freezing-rain days, 9) number of cold days and 10) number of days with temperature fluctuations. The overall effort prediction accuracy for the winter season 2021-2022 was 71 %. The independent variables, the model's outcomes, and its results when applied to simulate the effects of LOS downgrading on a particular road stretch and estimating CO2 emission over the whole network, are discussed.

关键词： WRM simulation multi-linear regression GIS

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Ozone Trend Analysis in Natal (5.4°S, 35.4°W, Brazil) Using multi-linear regression and Empirical Decomposition Methods over 22 Years of Observations

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REMOTE SENSING 2024年第1期16卷 208页

作者： Bencherif, Hassan Pinheiro, Damaris Kirsch Delage, Olivier Millet, Tristan Peres, Lucas Vaz Begue, Nelson Bittencourt, Gabriela Martins, Maria Paulete Pereira da Silva, Francisco Raimundo Steffenel, Luiz Angelo Mbatha, Nkanyiso Anabor, Vagner Univ la Reunion Lab Atmosphere & Cyclones LACy UMR CNRS 8105Phys DeptFac Sci & TechnolMeteo Fr F-97400 St Denis France Univ Fed Santa Maria Ctr Tecnol Dept Engn Quim BR-97105900 Santa Maria RS Brazil Univ Fed Oeste Para Inst Engn & Geociencias BR-68035110 Santarem PA Brazil Inst Nacl Pesquisas Espaciais BR-12227010 Sao Jose Dos Campos SP Brazil Univ Reims LICIIS Lab LRC CEA DIGIT Lab Informat Calcul Intens & Image Simulat F-51097 Reims France Univ Zululand Dept Geog ZA-3886 Kwa Dlangezwa South Africa

Ozone plays an important role in the Earth's atmosphere. It is mainly formed in the tropical stratosphere and is transported by the Brewer-Dobson Circulation to higher latitudes. In the stratosphere, ozone can filter the incoming solar ultraviolet radiation, thus protecting life at the surface. Although tropospheric ozone accounts for only similar to 10%, it is a powerful GHG and pollutant, harmful to the health of the environment and living beings. Several studies have highlighted biomass burning as a major contributor to the tropospheric ozone budget. Our study focuses on the Natal site (5.40 degrees S, 35.40 degrees W, Brazil), one of the oldest ozone-observing stations in Brazil, which is expected to be influenced by fire plumes in Africa and Brazil. Many studies that examined ozone trends used the total atmospheric columns of ozone, but it is important to assess ozone separately in the troposphere and the stratosphere. In this study, we have used radiosonde ozone profiles and daily TCO measurements to evaluate the variability and changes of both tropospheric and stratospheric ozone separately. The dataset in this study comprises daily total columns of colocalized ozone and weekly ozone profiles collected between 1998 and 2019. The tropospheric columns were estimated by integrating ozone profiles measured by ozone sondes up to the tropopause height. The amount of ozone in the stratosphere was then deduced by subtracting the tropospheric ozone amount from the total amount of ozone measured by the Dobson spectrometer. It was assumed that the amount of ozone in the mesosphere is negligible. This produced three distinct time series of ozone: tropospheric and stratospheric columns as well as total columns. The present study aims to apply a new decomposition method named Empirical Adaptive Wavelet Decomposition (EAWD) that is used to identify the different modes of variability present in the analyzed signal. This is achieved by summing up the most significant Intrinsi

关键词： empirical decompositions EMD EAWD multi-linear regression ozone variability and trends stratospheric ozone tropospheric ozone southern tropics Brazil

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multi-linear regression of triple diffusive convectively heated boundary layer flow with suction and injection: Lie group transformations

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2023年第1期37卷

作者： Mamatha, S. U. Devi, R. L. V. Renuka Ahammad, N. Ameer Shah, Nehad Ali Rao, B. Madhusudhan Raju, C. S. K. Khan, M. Ijaz Guedri, Kamel Kristu Jayanti Coll Dept Math Kothanur PO Bengaluru 560077 Karnataka India Sri Venkateswara Univ Dept Math Tirupati Andhra Pradesh India Univ Tabuk Fac Sci Dept Math POB 741 Tabuk 71491 Saudi Arabia Sejong Univ Dept Mech Engn Seoul 05006 South Korea Univ Technol & Appl Sci Fac Math Dept Informat Technol Muscat Oman GITAM Sch Sci GSS Dept Math Bengaluru Karnataka India Riphah Int Univ I14 Dept Math & Stat Islamabad 44000 Pakistan Lebanese Amer Univ Dept Mech Engn Beirut Lebanon Umm Al Qura Univ Coll Engn & Islamic Architecture Mech Engn Dept POB 5555 Mecca 21955 Saudi Arabia

This work analyzes the two-dimensional flow of an incompressible magneto-hydrodynamic fluid over linear stretching sheet in the presence of suction or injection and convective boundary conditions. A scaling group transformation method is applied to the flow governing equations. The system remains invariant due to the relation between the transformation parameters. Upon finding three absolute invariants, third-order ordinary differential equations (ODEs) corresponding to momentum equation and second-order ODEs corresponding to energy and diffusion equations are derived. Shooting technique (R-K fourth-order) is applied to work out the flow equations numerically. MATLAB is used for the simulation and the results are exhibited through graphs. The computational results are validated with the published research work and a modest concurrence was found. The main outcome of this study is found to be that raising values of f(si) and Gamma(1) decline the friction, whereas Gamma(1) and Gamma(2) show the opposite (increasing). The rising values of f(si) and M in addition to M and Gamma(2) show a decline in friction factor. The Nusselt number values are improved as raising values of Pr versus f(si) and Pr versus M. It is very clear the monotonically increasing M versus f(si) and strictly increasing f(si )versus M cases. It is very clear the mass-transfer rate is smoothly improved Le(1) versus Le(2) and strictly increased Le(1) versus Gamma(1).

关键词： Lie group suction/injection triple diffusive convection convective boundary magneto-hydrodynamic multi-linear regression

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Intelligent WSN Localization using multi-linear regression and a Mobile Anchor Node 13

Intelligent WSN Localization using Multi-Linear Regression a...

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13th International Conference on Information and Communication Systems (ICICS)

作者： Yaseen, Tuqa M. Bani Awad, Fahed H. Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid Jordan

ISBN: (纸本)9781665480970

As the applications of Wireless Sensor Network are increasingly associated with the rapidly growing field of the Internet of Things, resolving the problem of precisely locating sensor nodes has become a pressing need. As a result, several location identification methodologies have been proposed to address this need. This paper proposes a machine learning-based algorithm that allows the sensor node to identify its own location accurately using a single mobile anchor node. The mobile anchor node moves along a relatively short straight path, reducing the energy consumption associated with its movement as well as the total time needed to identify the locations of all sensor nodes. The proposed machine learning model is based on the multi-linear Regressing model and it is trained offline, reducing the burden on the resource-constraint sensor nodes. The performance evaluation results show that the proposed algorithm can achieve location identification accuracy of less than half a meter.

关键词： Wireless Sensor Networks Location Identification Machine Learning Mobile Anchor Node multi-linear regression

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Application of random forest and multi-linear regression methods in downscaling GRACE derived groundwater storage changes

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2021年第5期66卷 874-887页

作者： Jyolsna, P. J. Kambhammettu, B. V. N. P. Gorugantula, Saisrinivas Indian Inst Technol Hyderabad Dept Civil Engn Hyderabad Telangana India

The advent of Gravity Recovery and Climate Experiment (GRACE) has opened the doors for remote monitoring of gravitational changes and its derivatives across the globe, but received less attention due to poor spatial and temporal representation. Statistical models of varying complexity are commonly employed to downscale the GRACE datasets for use with local to regional applications. This study presents the application of two commonly employed machine learning models, multi-linear regression (MLR) and random forest (RF), in spatially downscaling (from 1 degrees to 0.25 degrees) the GRACE-derived terrestrial water storage anomalies (TWSA) by establishing a correlation with various land surface and hydroclimatic variables. The downscaled TWSA was further converted into groundwater storage anomalies. Applicability of the proposed methods was tested on four contrasting hydrogeological basins of India. For each basin, the significant predictor variables were considered to establish the relations. Seasonal groundwater levels observed in 236 wells during 2006-2015 were used for method validation and accuracy assessment. We observed a close match between GRACE-derived groundwater levels and the measurements for three of the four basins (r = 0.40-0.92, Root mean square error (RMSE) = 3.6-10.5 cm). Our results indicate that the predictor variables to downscale TWSA should be considered cautiously based on the hydrogeological, topographical, and meteorological characteristics of the basin.

关键词： GRACE statistical downscale multi-linear regression random forest groundwater storage India

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Application of artificial neural network and multi-linear regression techniques in groundwater quality and health risk assessment around Egbema, Southeastern Nigeria

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ENVIRONMENTAL EARTH SCIENCES 2023年第3期82卷 77-77页

作者： Akakuru, Obinna Chigoziem Adakwa, Chidi Bello Ikoro, Diugo Okereke Eyankware, Moses Oghenenyoreme Opara, Alexander Iheanyi Njoku, Adora Olivia Iheme, Kenneth Obinna Usman, Ayatu Fed Univ Technol Owerri Dept Geol Owerri Imo Nigeria Dennis Osadabe Univ Fac Sci Dept Geol Warri Delta Nigeria Nnamdi Azikiwe Univ Dept Geol Sci Awka Anambra Nigeria Univ Ilorin Dept Geol Ilorin Nigeria Alex Ekwueme Fed Univ Dept Phys Geol & Geophys Ndufu Alike Ebonyi Nigeria

This paper examined the efficiency of artificial neural network (ANN) and multivariate linear regression (MLR) models in the prediction of groundwater quality parameters such as ecological risk index (ERI), pollution load index (PLI), metal pollution index (MPI), Nemerow pollution index (NPI), and geoaccumulation index (Igeo). 40 groundwater samples were collected systematically and analyzed for mainly heavy metals. Results revealed that adopting measured parameters is effective in modeling the parameters with high level of accuracy. Contamination factor results reveal that Ni, Zn, Pb, Cd, and Cu have relatively low values < 1 within the region while the Iron values ranged from low contamination to very high contamination (> 6). PLI, MPI, and ERI results indicated low pollution. NPI results indicated that the majority of the samples were heavily polluted. Quantification of Contamination results revealed that most of the sample's quality was geogenically influenced. Igeo results revealed that most of the samples had extreme pollution. The health risk assessment results revealed that children are substantially prone to more health risk more than adults. The ANN and MLR models showed a high effective tendency in the prediction of ERI, PLI, MPI, NPI and Igeo. Principal component analysis results showed appreciable variable loadings while the correlation matrix results reveal that there exists weak and positive correlation amongst elements. Based on the outcome of this study, this research recommends the use of ANN and MLR models in the prediction of groundwater quality parameters as they yielded positive, reliable, acceptable, and appropriate accuracy performances.

关键词： Artificial neural networks multi-linear regression Health risk assessment Quantification of contamination Geoaccumulation index Ecological risk index

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Modelling PM2.5 for Data-Scarce Zone of Northwestern India using multi linear regression and Random Forest Approaches

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ANNALS OF GIS 2023年第3期29卷 415-427页

作者： Sharma, Vipasha Ghosh, Swagata Dey, Sagnik Singh, Sultan Amity Univ Amity Inst Geoinformat & Remote Sensing AIGIRS Noida Uttar Pradesh India Cent Univ Karnataka Kadaganchi Sch Earth Sci Dept Geog Kalaburagi Karnataka India Indian Inst Technol Delhi Ctr Atmospher Sci New Delhi India Haryana Space Applicat Ctr Node Gurugram Haryana India Cent Univ Karnataka Sch Earth Sci Dept Geog Aland Rd Kalaburagi 585367 Karnataka India

PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), multi-linear regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 mu gm(-3)) and post-monsoon (112.93 mu gm(-3)), whereas the wet monsoon (44.56 mu gm(-3)) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.

关键词： MAIAC PM2 5 hybrid model random forest multi-linear regression

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The QSPR models to predict the solubility of CO₂ in ionic liquids based on least-squares support vector machines and genetic algorithm-multi linear regression

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JOURNAL OF MOLECULAR LIQUIDS 2017年 225卷 521-530页

作者： Mehraein, Iliad Riahi, Siavash Univ Tehran Coll Engn Fac Chem Engn Inst Petr Engn Tehran Iran

Global warming is an issue of high concern which is mostly caused by growing concentrations of carbon dioxide in the atmosphere. Many novel technologies offer different solutions to decline carbon dioxide emissions to the environment. Ionic liquids (ILs) are counted to be highly promising media for CO2 capture in the near future. Due to costly nature of ionic liquids and time consuming laboratory research procedures, modeling and prediction of solubility of CO2 based on the structure of ILs are highly required. Some studies on this field demonstrate the relationship between the structure and the CO2 absorption capacity of ILs. One of the modeling approaches for stating this relationship is quantitative structure-property relationship (QSPR). In this work, an efficient approach based on the combination of genetic algorithm-multi linear regression (GA-MLR) and least-squares support vector machines (LS-SVM) was utilized to build a nonlinear QSPR model. The nonlinear model can give very satisfactory prediction results: the square of correlation coefficient (R-2) and the root mean square error (RMSE) were 0.962 and 0.015, respectively for the whole dataset. In addition, another QSPR model, multi-linear regression (MLR), was also implemented and R-2 and RMSE were 0.876 and 0.027, respectively. The results demonstrate that the LS-SVM model drastically enhances the ability of prediction in QSPR studies and is superior to MLR one. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Ionic liquids Solubility Carbon dioxide QSPR multi-linear regression Support vector machines

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Parametric Modeling of the Coupling Channel of Conducted Interference Based on multi-linear regression Model

Parametric Modeling of the Coupling Channel of Conducted Int...

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IEEE MTT-S International Conference on Numerical Electromagnetic and multiphysics Modeling and Optimization (NEMO)

作者： Wang, Shunxin Dai, Fei Zheng, Tao Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781467387620

In electromagnetic compatibility testing, the conducted electromagnetic emission (EMI) of the equipment under test (EUT) are required to meet certain standards of electromagnetic compatibility in order to control its external conducted emission. However, sometimes there is no way to eliminate the internal interference sources of EUT, so the only way to reduce the EMI is to change the coupling channel from the internal interference sources to the external test port. Therefore, it is necessary to understand the characteristic of the coupling channel, so that the EMI of the EUT can be effectively analyzed and restrained. In this paper, a frequency-domain parametric modeling method model is proposed, and an example to verify the algorithm is presented. The example shows that the modeling method can reflect the characteristic of coupling channel in frequency domain.

关键词： coupling channel multi-linear regression coefficient matrix frequency-domain parametric modeling

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