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Journal articles on the topic 'GRNNs'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Catasus, Miguel, Wayne Branagh, and Eric D. Salin. "Improved Calibration for Inductively Coupled Plasma-Atomic Emission Spectrometry Using Generalized Regression Neural Networks." Applied Spectroscopy 49, no. 6 (1995): 798–807. http://dx.doi.org/10.1366/0003702953964444.

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Artificial neural networks have been recently used in different fields of science in applications ranging from pattern recognition to semi-quantitative analysis. In this work, two types of neural networks were applied to the problems of spectral interferences, matrix effects, and the measurement drift in ICP-AES. Their performance was compared to that of the more conventional technique of multiple linear regressions (MLR). The two types of neural networks examined were “traditional” multilayer perceptron neural networks and generalized regression neural networks (GRNNs). The GRNN is comparable

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Khan, Muhammad Shahbaz, Mir Ghulam Hyder Talpur, and Muhammad Aslam. "Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada." VFAST Transactions on Mathematics 12, no. 1 (2024): 415–23. http://dx.doi.org/10.21015/vtm.v12i1.1894.

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This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviat

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Dang, Hieu V., and Witold Kinsner. "Optimal Colour Image Watermarking Using Neural Networks and Multiobjective Memetic Optimization." International Journal of Neural Networks and Advanced Applications 9 (March 11, 2022): 23–32. http://dx.doi.org/10.46300/91016.2022.9.5.

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This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is

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Wiangkham, Attasit, Niti Klinkaew, Prasert Aengchuan, Pansa Liplap, Atthaphon Ariyarit, and Ekarong Sukjit. "Experimental and optimization study on the effects of diethyl ether addition to waste plastic oil on diesel engine characteristics." RSC Advances 13, no. 36 (2023): 25464–82. http://dx.doi.org/10.1039/d3ra04489k.

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The combined NSGA-II algorithm and GRNNs model accurately predicted the multi-objective function, enabling identification of the optimal DEE percentage in WPO and engine operating condition to achieve maximum engine efficiency and minimum emissions.

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Günaydın, Kemal, and Ayten Günaydın. "Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey." Mathematical Problems in Engineering 2008 (2008): 1–20. http://dx.doi.org/10.1155/2008/919420.

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Three different artificial neural network (ANN) methods, namely, feed-forward back-propagation (FFBP), radial basis function (RBF), and generalized regression neural networks (GRNNs) were applied to predict peak ground acceleration (PGA). Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction

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Zhang Geng Xu Hao Wen Wu. "The Study on Vocal Print Recognition basing on the GRNNs." International Journal of Digital Content Technology and its Applications 6, no. 14 (2012): 291–97. http://dx.doi.org/10.4156/jdcta.vol6.issue14.36.

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Feng, Yu, Daozhi Gong, Xurong Mei, and Ningbo Cui. "Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau." Hydrology Research 48, no. 4 (2016): 1156–68. http://dx.doi.org/10.2166/nh.2016.099.

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Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The pe

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Hussain, Hafezali Iqbal, Nazratul Aina Mohamad Anwar, and Mohd Shahril Ahmad Razimi. "A generalised regression neural network model of financing imbalance: Shari’ah compliance as the roadmap for sustainability of capital markets." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5387–95. http://dx.doi.org/10.3233/jifs-189023.

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The current study looks at the impact of compliance to Shari’ah principles on the capital structure for Malaysian firms. Examination of impact of compliance is based on the classification by the Securities Commission of Malaysia. Given that the literature on adjustment tends to ignore non-linear models, the current study utilises Generalised Regression Neural Network (GRNNs). Results are compared to conventional panel data regression models via performing a hold-out sample. Initial results confirm stability of the data allowing predictive ability. The results indicate that compliant firms tend

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STUBBERUD, PETER. "A VECTOR MATRIX REAL TIME RECURSIVE BACKPROPAGATION ALGORITHM FOR RECURRENT NEURAL NETWORKS THAT APPROXIMATE MULTI-VALUED PERIODIC FUNCTIONS." International Journal of Computational Intelligence and Applications 08, no. 04 (2009): 395–411. http://dx.doi.org/10.1142/s1469026809002667.

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Unlike feedforward neural networks (FFNN) which can act as universal function approximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTRBP) algorithm in a vector matrix form is developed for a two-layer globally recursive neural network that has multiple delays in its feedback path. This algorithm has been evaluated on two GRNNs that approximate both an analytic and nonanalytic periodic multi-valued function that a feedforward neural network is not capable of approximating.

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Joshua, Vinson, Selwin Mich Priyadharson, and Raju Kannadasan. "Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu." Agronomy 11, no. 10 (2021): 2068. http://dx.doi.org/10.3390/agronomy11102068.

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain hi

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11

Tkachenko, Roman, Ivan Izonin, Natalia Kryvinska, Ivanna Dronyuk, and Khrystyna Zub. "An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble." Sensors 20, no. 9 (2020): 2625. http://dx.doi.org/10.3390/s20092625.

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The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of predict

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Li, Juan, Zhiqiang Xiao, Rui Sun, and Jinling Song. "Retrieval of the Leaf Area Index from Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance Based on Unsupervised Domain Adaptation." Remote Sensing 14, no. 8 (2022): 1826. http://dx.doi.org/10.3390/rs14081826.

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Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map

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Klinkaew, Niti, Attasit Wiangkham, Atthaphon Ariyarit, et al. "Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework." Results in Engineering 24 (December 2024): 103072. http://dx.doi.org/10.1016/j.rineng.2024.103072.

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Song, Dengwei, Hongmei Liu, Le Qi, and Bo Zhou. "A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers." Complexity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4763612.

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A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual e

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veer, Som, M. Kumari, A. Pramanik, B. Lakshmaiah, B. Godara, and PL Parameswari. "A Predictive Approach for Evaluating Thermo-Physical Properties of Nano fluids Using Artificial Intelligence Algorithms." 3 2, no. 3 (2023): 55–61. http://dx.doi.org/10.46632/jdaai/2/3/10.

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Artificial Intelligence (AI) algorithms are increasingly being employed as substitutes for conventional methods or as components within integrated systems. They have demonstrated effectiveness in addressing complex applied problems across various domains, gaining popularity in the present context. AI approaches exhibit the ability to learn from patterns, tolerate faults by handling noisy data, and manage non-linear problems. Once trained, they excel in generalization and fast estimation. This survey presents a comprehensive review of AI algorithms developed for investigating nanoﬂuid-related i

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Aengchuan, Prasert, Attasit Wiangkham, Niti Klinkaew, Kampanart Theinnoi, and Ekarong Sukjit. "Prediction of the influence of castor oil–ethanol–diesel blends on single-cylinder diesel engine characteristics using generalized regression neural networks (GRNNs)." Energy Reports 8 (November 2022): 38–47. http://dx.doi.org/10.1016/j.egyr.2022.10.113.

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Liu, Duanyang, Linqing Yang, Kun Jia, et al. "Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods." Remote Sensing 10, no. 10 (2018): 1648. http://dx.doi.org/10.3390/rs10101648.

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Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS

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Carbureanu, Madalina. "A COMPARATIVE STUDY OF DEEP LEARNING METHODS APPLIED FOR WASTEWATER pH NEUTRALIZATION PROCESS MODELLING." Romanian Journal of Petroleum & Gas Technology 5 (76), no. 2 (2024): 131–46. https://doi.org/10.51865/jpgt.2024.02.09.

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In a wastewater treatment plant (WWTP) there are several different, intricate processes with a dynamic and nonlinear behaviour. The fact that these processes are nonlinear, some of them having a high degree of nonlinearity, as is the wastewater pH neutralization process, comes with a number of problems related to their modelling and control. The identification of any method that can be used to simplify the modelling and control of such a high nonlinear process, it is a desideratum to ensure a quality effluent of the plant, because its water quality is affected by the treated wastewater dischar

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Kartal, Serkan, Mustafa Oral, and Buse Melis Ozyildirim. "Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map." International Journal of Applied Mathematics and Computer Science 28, no. 2 (2018): 411–24. http://dx.doi.org/10.2478/amcs-2018-0031.

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Abstract In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-proc

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20

Vilavicencio-Arcadia, Edgar, Silvana G. Navarro, Luis J. Corral, et al. "Application of Artificial Neural Networks for the Automatic Spectral Classification." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/1751932.

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Classification in astrophysics is a fundamental process, especially when it is necessary to understand several aspects of the evolution and distribution of the objects. Over an astronomical image, we need to discern between stars and galaxies and to determine the morphological type for each galaxy. The spectral classification of stars provides important information about stellar physical parameters like temperature and allows us to determine their distance; with this information, it is possible to evaluate other parameters like their physical size and the real 3D distribution of each type of o

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21

Singh, Siddhartha Kumar, Harlal Singh Mali, Deepak Rajendra Unune, Szymon Wojciechowski, and Dominik Wilczyński. "Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study." Processes 10, no. 4 (2022): 755. http://dx.doi.org/10.3390/pr10040755.

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Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Ne

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22

Li, Bo, and Yuanqiang Lian. "A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction." Applied Sciences 15, no. 10 (2025): 5575. https://doi.org/10.3390/app15105575.

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Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR)

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23

Yang, Qiujuan, and Jiaxiao Zhang. "Research on the Analysis of students’ English Learning Behavior and Personalized Recommendation Algorithm based on Machine learning." Scalable Computing: Practice and Experience 26, no. 1 (2025): 450–57. https://doi.org/10.12694/scpe.v26i1.3856.

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The goal of this study is to create a personalized recommendation system for English learning resources by analysing students’ English learning behaviors using a Generalized Regression Neural Network (GRNN). For efficient language acquisition, tailored educational support is essential due to the diversity of students’ linguistic backgrounds and learning demands. Performance ratings, personal preferences, and the amount of time students spent on various content categories were among the data gathered for this study on how students interacted with English language learning materials. Our initial

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Peeters, Piet-Hein. "Grens." Zorg + Welzijn 23, no. 12 (2017): 7. http://dx.doi.org/10.1007/s41185-017-0181-5.

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Robert, Corentin, Francisco Prista von Bonhorst, Geneviève Dupont, Didier Gonze, and Yannick De Decker. "Role of tristability in the robustness of the differentiation mechanism." PLOS ONE 20, no. 3 (2025): e0316666. https://doi.org/10.1371/journal.pone.0316666.

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During cell differentiation, identical pluripotent cells undergo a specification process marked by changes in the expression of key genes, regulated by transcription factors that can inhibit the transcription of a competing gene or activate their own transcription. This specification is orchestrated by gene regulatory networks (GRNs), encompassing transcription factors, biochemical reactions, and signalling cascades. Mathematical models for these GRNs have been proposed in various contexts, to replicate observed robustness in differentiation properties. This includes reproducible proportions o

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Jansson, Oscar. "Maskinkroppens gräns." Tidskrift för litteraturvetenskap 51, no. 1-2 (2021): 153–71. http://dx.doi.org/10.54797/tfl.v51i1-2.1747.

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Boundaries of the Machine Body: Violence, Immunity and Media Assemblages in The Last of Us This article examines the portrayal of bodily boundaries in the videogame series The Last of Us. Drawing on theories of media ecology and posthumanism (most notably Deer’s notion of radical animism, Haraway’s theories of the cyborg, and Fuller’s account of media assemblages), three aspects of this portrayal are described: first, the game’s narrativization of bodily violence through an amalgamation of the player’s sensory systems with media technologies; second, the game’s depiction of monstrous corporeal

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Callens, Johan, and Johan Callens. "Grens/gevallen." Documenta 12, no. 4 (2019): 215–48. http://dx.doi.org/10.21825/doc.v12i4.10717.

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Palmer, J. D., and J. A. Page. "Gobsmacking grins'." British Dental Journal 171, no. 1 (1991): 28–29. http://dx.doi.org/10.1038/sj.bdj.4807595.

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29

Głąbińska, Dominika. "Svenskhet som gräns." Studia Scandinavica, no. 1 (21) (December 17, 2017): 175–90. http://dx.doi.org/10.26881/ss.2017.21.12.

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The goal of the article “Svenskhet som gräns, Swedishness as a border” is to present problems with the concept of Swedishness from another perspective than it has been discussed in the Swedish media. The article analyses responses from Swedish political parties with regard to “Swedishness” and democracy, and it provides an insight to the contemporary situation in Sweden.

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Karlsson, Gunnel, Beata Losman, and Louise Lönnroth. "Vid kunskapens gräns." Tidskrift för genusvetenskap 4, no. 2 (2022): 43–50. http://dx.doi.org/10.55870/tgv.v4i2.5761.

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Ett viktigt metodproblem för kvinnoforskare är att källmaterialet ofta är utformat av och för män. Ett exempel är skattelängderna där endast mannens namn sattes ut medan hustru, barn och tjänstefolk angavs som streck i olika kolumner. Författarna visarfrån sin egen forskning att förvaltningsmaterial om kvinnor finns men måste behandlas med stor försiktighet.

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van Oers, Sanne. "Boven de grens." Advocatenblad 102, no. 5 (2022): 3. http://dx.doi.org/10.5553/ab/0165-13312022102005001.

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Jacobs, Evelien. "DE GRENS OVERGAAN." TvPO 17, no. 1 (2022): 50. http://dx.doi.org/10.1007/s12503-022-0919-1.

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Eker, Mark, and Henk Van Houtum. "Ontwerp de grens." AGORA Magazine 28, no. 4 (2012): 6–8. http://dx.doi.org/10.21825/agora.v28i4.2396.

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Kleinherenbrink, Arjen, and Simon Gusman. "Over de grens." Algemeen Nederlands Tijdschrift voor Wijsbegeerte 106, no. 3 (2014): 257–61. http://dx.doi.org/10.5117/antw2014.3.klei.

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. "Over de grens." Supervisie en Coaching 25, no. 2 (2008): 123–30. http://dx.doi.org/10.1007/bf03099288.

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. "Over de grens." Maatwerk 7, no. 6 (2006): 270–71. http://dx.doi.org/10.1007/bf03070749.

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Berenbaum, May. "Frass-Eating Grins." American Entomologist 49, no. 3 (2003): 132–33. http://dx.doi.org/10.1093/ae/49.3.132.

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ten Voorde, Jeroen. "Over de grens." PROCES 96, no. 4 (2017): 265–66. http://dx.doi.org/10.5553/proces/016500762017096004001.

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Konings, Jos. "Over de grens." Tijdschrift voor VerpleeghuisGeneeskunde 34, no. 2 (2009): 42. http://dx.doi.org/10.1007/bf03081357.

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Weinstein, Michael L., Chad M. Jaenke, Hasiba Asma, et al. "A novel role for trithorax in the gene regulatory network for a rapidly evolving fruit fly pigmentation trait." PLOS Genetics 19, no. 2 (2023): e1010653. http://dx.doi.org/10.1371/journal.pgen.1010653.

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Animal traits develop through the expression and action of numerous regulatory and realizator genes that comprise a gene regulatory network (GRN). For each GRN, its underlying patterns of gene expression are controlled by cis-regulatory elements (CREs) that bind activating and repressing transcription factors. These interactions drive cell-type and developmental stage-specific transcriptional activation or repression. Most GRNs remain incompletely mapped, and a major barrier to this daunting task is CRE identification. Here, we used an in silico method to identify predicted CREs (pCREs) that c

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Song, Lu-Kai, Guang-Chen Bai, Cheng-Wei Fei, and Jie Wen. "Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling." Advances in Materials Science and Engineering 2018 (October 16, 2018): 1–16. http://dx.doi.org/10.1155/2018/3469465.

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To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life pre

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Chen, Chi-Kan. "Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections." Statistical Applications in Genetics and Molecular Biology 20, no. 4-6 (2021): 121–43. http://dx.doi.org/10.1515/sagmb-2020-0054.

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Abstract The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacen

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Hilal, Muhammad, Haider TH. Salim ALRikabi, and Ibtisam A. Aljazaery. "Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization." Wasit Journal of Engineering Sciences 11, no. 3 (2023): 45–56. http://dx.doi.org/10.31185/ejuow.vol11.iss3.451.

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The Generalized Recurrent Neural Network (GRNN) and Invasive Weed Optimization (IWO) algorithms are two powerful techniques that can be used to optimize motor drive speed. GRNN is a type of artificial neural network designed to process time-series data, while IWO is a metaheuristic optimization technique inspired by the behavior of invasive weed species. To optimize motor drive speed using GRNN and IWO algorithms, data on motor performance over time must be collected and used to train a GRNN model that can predict future motor performance based on past performance. By optimizing the parameters

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Zhou, Xin, and Xiaodong Cai. "Inference of differential gene regulatory networks based on gene expression and genetic perturbation data." Bioinformatics 36, no. 1 (2019): 197–204. http://dx.doi.org/10.1093/bioinformatics/btz529.

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Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity

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Herawati, Sri, and M. Latif. "Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network." JURNAL INFOTEL - Informatika Telekomunikasi Elektronika 8, no. 2 (2016): 132. http://dx.doi.org/10.20895/infotel.v8i2.124.

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Abstract—The method of time series suitable for use when it checks each data patterns systematically and has many variables, such as in the case of crude oil prices. One study that utilizes the methods of time series is the integration between Ensemble Empirical Mode Decomposition (EEMD) and neural network algorithms based on Polak-Ribiere Conjugate Gradient (PCG). However, PCG requires setting free parameters in the learning process. Meanwhile, the appropriate parameters are needed to get accurate forecasting results. This research proposes the integration between EEMD and Generalized Regress

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Yang, Pengfei, Xianbo Sun, Li Zhu, Yuhan Wu, and Baofu Dai. "Load Identification Method Based on ISMA-GRNN." Mathematical Problems in Engineering 2022 (March 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/8056696.

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The noninvasive load monitoring method carries out load identification after event detection and feature extraction of load data. At present, nonload intrusive load monitoring faces the problems of low load identification accuracy and long load identification time. In order to solve these problems, a load identification method based on the improved slime mould algorithm-generalized regression neural network (ISMA-GRNN) is proposed. Firstly, by adding mutation operation in slime mould algorithm (SMA) position update, the global optimization ability of SMA is improved. Then, the improved slime m

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Zhao, Huiru, and Sen Guo. "Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/217630.

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Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country. In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting. The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems. Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm o

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Wu, Cheng-Wei, Wu-Xing Zhou, Guofeng Xie, Xue-Kun Chen, Dan Wu, and Zhi-Qiang Fan. "Enhancement of thermoelectric performance in graphenylene nanoribbons by suppressing phonon thermal conductance: the role of phonon local resonance." Nanotechnology 33, no. 21 (2022): 215402. http://dx.doi.org/10.1088/1361-6528/ac5288.

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Abstract Based on the method of non-equilibrium Green’s function, we investigate the thermal transport and thermoelectric properties of graphenylene nanoribbons (GRNRs) with different width and chirality. The results show that the thermoelectric (TE) performance of GRNRs significantly increases with decreasing ribbon width, which stems from the reduction of thermal conductance. In addition, by changing the ribbon width and chirality, the figure of merit ( Z T ) can be controllably manipulated and maximized up to 0.45 at room temperature. Moreover, it is found that the Z T value of GRNRs with b

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Nivina, Aleksandra, Sur Herrera Paredes, Hunter B. Fraser, and Chaitan Khosla. "GRINS: Genetic elements that recode assembly-line polyketide synthases and accelerate their diversification." Proceedings of the National Academy of Sciences 118, no. 26 (2021): e2100751118. http://dx.doi.org/10.1073/pnas.2100751118.

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Assembly-line polyketide synthases (PKSs) are large and complex enzymatic machineries with a multimodular architecture, typically encoded in bacterial genomes by biosynthetic gene clusters. Their modularity has led to an astounding diversity of biosynthesized molecules, many with medical relevance. Thus, understanding the mechanisms that drive PKS evolution is fundamental for both functional prediction of natural PKSs as well as for the engineering of novel PKSs. Here, we describe a repetitive genetic element in assembly-line PKS genes which appears to play a role in accelerating the diversifi

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S, Shafana, Kripa Pappachan, Abida P.S, Acharya Arpita, G. Bhavya, and Melna Mary C.J. "In silico Design and Validation of CRISPR/Cas9 gRNAs for Enhancing Drought Tolerance and Yield in Rice." Journal of Advances in Biology & Biotechnology 27, no. 12 (2024): 1051–59. https://doi.org/10.9734/jabb/2024/v27i121852.

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Rice is a staple food crop globally, and its yield is significantly impacted by drought, posing a threat to food security. Several genes have been implicated in drought response and yield regulation, which can be targeted using CRISPR/Cas9 genome editing techniques to develop improved rice cultivars. This study aimed to design guide RNAs (gRNAs) for drought-responsive and yield-determining genes in rice, including flavanone 3-hydroxylase-1 (OsF3H-1), chalcone synthase 31 (OsCHS31), nodulin/SWEET12 (OsSWEET12), MYB47, and OsKALA3, utilizing bioinformatics tools. Genome sequences were retrieved

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