The scientific and research community has used Neural Designer widely. Many articles use this program to support or even fully develop their research.
- Application of neural networks for prediction of doubly fed induction generator’s equivalent circuit parameters used in wind generators.
- Computerized Detection of JWH Synthetic Cannabinoids Class Membership Based on Machine Learning Algorithms and Molecular Descriptors.
- Experimental Investigation on use of Molecular Sieve4A as Desiccant in Heatless Desiccant Air Dryer.
- Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks.
- Implication of Artificial Intelligence Approach On Groundwater Level.
- Key Features of the Performance of Chinese Musicians.
- Deep neural networks applications in the study of a geological indicator.
- Classification of Pb recovery affecting factors with artificial ıntelligence based algorithms.
- PAD: A Pancreatic Cancer Detection based on Extracted Medical Data through Ensemble Methods in Machine Learning.
- Flexural strength estimation of basalt fiber reinforced fly-ash added gypsum based composites.
- Machine learning modeling for solubility prediction of recombinant antibody fragments in four different E. coli strains.
- Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations.
- Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks.
- N, S co–doped biocarbon for supercapacitor application: Effect of electrolytes concentration and modeling with an artificial neural network.
- Investigations into succinic acid fermentation.
- Modeling resilient modulus of fine-grained materials using different statistical techniques.
- Prediction of the Onset of Diabetes Using Artificial Neural Network and Pima Indians Diabetes Dataset.
- A Novel Integration of Hyper-spectral Imaging and Neural Networks to Process Waste Electrical and Electronic Plastics.
- Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites.
Application of neural networks for prediction of doubly fed induction generator’s equivalent circuit parameters used in wind generators
Due to its merits, the doubly fed induction generator (DFIG) finds its application in sizeable grid-connected power systems. Its equivalent circuit parameter typically represents DFIG by stator and rotor winding resistance,
reactance and mutual reactance.
In this paper, the equivalent circuit parameters of DFIG are predicted using deep neural networks created and simulated with ‘Neural Designer ‘. This article uses Neural Designer to improve their predictive model with different algorithms available in the program, such as selection order and neuron selection features. This algorithm increases the accuracy of the neural network and optimizes the computational cost.
- Ramakrishna(2021). Application of Neural Networks for Prediction of Double Fed Induction Generator’s Equivalent Circuit Parameters used in Wind Generators. IRJCS:: International Research Journal of Computer Science, Volume VIII, 23-31.
Computerized Detection of JWH Synthetic Cannabinoids Class Membership Based on Machine Learning Algorithms and Molecular Descriptors
The detection of the JWH synthetic cannabinoids and non-JWH cannabinoids and discriminating them from other compounds of forensic interest is extremely important. These compounds have very limited legal uses. Hence, there are very few studies about their pharmacological activity and toxicity. The most important characteristic of any system screening for these drugs of abuse is its capacity to recognize correctly the class identity of the positive samples, which, according to the requirements for a forensic tool, should not be misclassified under any circumstances.
This paper uses Neural Designer to create a predictive model for detecting these substances. The researcher draws statistical conclusions with the help of model analysis utilities included in Neural Designer.
- Burlacu, C. M., Praisler, M., & Burlacu, A. C. (2022, May). Computerized Detection of JWH Synthetic Cannabinoids Class Membership Based on Machine Learning Algorithms and Molecular Descriptors. In 2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) (pp. 1-5). IEEE.
Experimental Investigation on use of Molecular Sieve4A as Desiccant in Heatless Desiccant Air Dryer
Moisture in compressed air, even in small amounts, can cause damage to the final products. As a result, removing moisture from compressed air is particularly desirable, and to accomplish that, a heatless desiccant air dryer can be used. Therefore, the usage of solid desiccant molecular sieve 4A in a heatless air dryer with a capacity of 10 CFM is investigated in this research.
Using Neural Designer, The researcher builds a neural network created artificially to forecast the dew point temperature in a heatless air dryer over a more extended period. All input parameters, such as flow rate, relative humidity, pressure, etc., have been kept constant, and the model predicts the outlet dew point temperature over time.
The training and analysis of the model are performed using the Model Training and Testing analysis features from Neural Designer and help to conclude the efficiency of this desiccant with great accuracy.
- D’souza, A. J., & Brahmbhatt, P. K. Experimental Investigation on use of Molecular Sieve4A as Desiccant in Heatless Desiccant Air Dryer. zeolites (molecular sieve), 4, 5.
Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks
This study aimed to develop travel time prediction models for transit buses to assist decision-makers in improving service quality and patronage. Six months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, D.C., was used for this study.
In this case, the researcher used a neural network for approximate Travel time for Washington buses with various models designed and trained in Neural Designer.
The main results of this study were accurate, with a Normalized Selection Squared Error between 0.04 and 0.26 in the different parts of the day.
- Arhin, S., Manandhar, B., & Baba-Adam, H. (2020). Predicting travel times of bus transit in Washington, DC, using artificial neural networks. Civil Engineering Journal, 6(11), 2245-2261.
Implication of Artificial Intelligence Approach On Groundwater Level
Groundwater is considered one of the most critical water resources for humans and the environment, but now this is declining faster. Various models have been developed to analyze the hydrological aspects of water resources. However, due to the complexity of data acquisition and extensive data requirements for physical models, they are tough to model the water resources problems.
The research aims to simulate and model groundwater levels and find how much impact climate change imposes in groundwater scenarios using Artificial Intelligence as an alternative approach over physical-based models. In this case, the optimal neural network architecture is obtained after several trials with a different number of layers and neurons using Neural Designer and its features.
In this paper, the impact of climate change on groundwater levels is studied using an artificial neural network approach, concluding that we are in an alarming situation.
- Saini, P., & Srivastava, R. (2020). Implication of Artificial Intelligence Approach On Groundwater Level. International Journal of Research in Engineering, Science and Management, 3(8), 356-359.
Key Features of the Performance of Chinese Musicians
The article is devoted to the methods of teaching saxophone playing within the framework of two classical approaches to teaching. The purpose of the article is to identify the features of two classical approaches to teaching the saxophone within the French and American traditions from the point of view of the peculiarities of the perception of these two techniques by Chinese students.
Regarding learning to play the saxophone, it is necessary to consider the methodological differences in techniques and methods of sound production. At the same time, in the future, it is necessary to investigate the methodology of teaching saxophone playing from the perspective of a polystylistic approach, when the goal of training musicians is the ability to play all styles at an equal quality level.
In this paper, ‘Neural Designer’ is used for analytics purposes without using complex and non-friendly statistical programs like R. The analytical capacities of Neural Designer allow different types of the researchers to perform analyses in a very easy, clear, and understandable way, even using tools like exporting charts or graphs.
- Xin, D. (2021). Key Features of the Performance of Chinese Musicians (Sample Case: Playing on the Saxophone). REVISTA GEINTEC-GESTAO INOVACAO E TECNOLOGIAS, 11(3), 610-623.
Deep neural networks applications in the study of a geological indicator
The differences between shallow neural networks and deep neural networks are considered. Finally, data from the operational exploration of an open pit mine are used to train different types of deep neural networks to predict a helpful indicator.
Neural Designer is used in this case to train several neural networks, compare their performance, and choose the best one for a case study using the different parameters provided by the program simply and concisely.
The research concludes in this case that a two-layer neural network provides excellent performance and balance between accuracy and computational cost. Moreover, a neural network with two two-layers with four neurons is the optimal solution because of the correlation with higher accuracy, lack of overtraining, and a simplified structure.
- Arsova-Borisova, K., & Hristov, V. DEEP NEURAL NETWORKS APPLICATIONS IN THE STUDY OF A GEOLOGICAL INDICATOR. SUSTAINABLE EXTRACTION AND PROCESSING OF RAW MATERIALS Учредители: Горно-геологический университет им. Св. Ивана Рильского, (2), 25-27.
Classification of Pb recovery affecting factors with artificial ıntelligence based algorithms
Lead (Pb) is one of the most widely used non-ferrous materials. Many methods have been developed to produce lead metal from initial raw materials or secondary resources.
The Artificial Neural Network structure was proposed with 4 inputs for predicting Pb recovery percentage. Four input parameters: temperature, NaCl concentration, time, and solid to liquid ratio values were selected based on physical considerations and the experimental test results.
In this case, Neural Designer provides a simple and complete way to perform the training and analysis of the Neural Network and provides a Normalized Squared Selection Error of 0.2374. Moreover, this model provided by Neural Designer allows the researcher to conclude that :
- There is a strong correlation between the Pb recovery amount with the Solid-liquid ratio and NaCl concentration
- The study’s outcomes can be assessed by other artificial and mathematical systems for a better understanding of input effects on Pb recovery amounts.
This statistical analysis indicates that lead recoveries up to 98 % could be obtained at a low pulp density with considerable NaCl concentration on the laboratory scale. Compared with previous studies, these results yielded very consistent findings in terms of Pb leach efficiency.
- Rusen, A., & Yildizel, S. A. (2018). Classification of Pb recovery effecting factors with artificial intelligence based algorithms. Journal of Engineering Research and Applied Science, 7(1), 811-817.
PAD: A Pancreatic Cancer Detection based on Extracted Medical Data through Ensemble Methods in Machine Learning
Pancreatic adenocarcinoma is one of the most dangerous types of cancer, and its diagnosis can be predicted by several indicators using a neural network.
This study’s data comes from Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and the University of Belgrade. This data set has a combined amount of 590 samples with 183 control samples, 208 benign hepatobiliary disease samples (such as chronic pancreatitis), and 199 with PDAC, all contained in a CSV file with 590 rows and 14 columns which are the different factors affecting the development of PDAC.
Neural Designer processes the dataset, and the researcher uses the dataset report utilities to pre-analyse the situation. Then, a division is made between Stages I, II, and Stages III, IV. After this, a model is made for each case to be predicted, and a conclusion is made using the tools provided by ‘Neural Designer ‘ such as calculating ROC curves and confusion matrices.
- Reddy, S., & Chandrasekar, M. (2022). PAD: A Pancreatic Cancer Detection based on Extracted Medical Data through Ensemble Methods in Machine Learning. International Journal of Advanced Computer Science and Applications, 13(2).
Flexural strength estimation of basalt fiber reinforced fly-ash added gypsum based composites
Gypsum and fiber-reinforced gypsum-based composites are widely used in many construction projects. Much research has been conducted on these types of composites; however, there are very limited literature studies focused on the estimation of flexural strength of fiber-reinforced gypsum-based composites. There is no current study on the mechanical strength estimation of these types of composites.
There is a large database of experimental information with 275 results with 5 inputs: fly ash, basalt, superplasticizer contents, water to binder ratio, and slump values are used to build and train the model.
In this case, ‘ Neural Designer ‘ calculates a neural network model with a good correlation between the flexural strength and predicted data.
- Yildizel, S. A., & Arslan, Y. (2018). Flexural strength estimation of basalt fiber reinforced fly-ash added gypsum based composites. Journal of Engineering Research and Applied Science, 7(1), 829-834.
Machine learning modeling for solubility prediction of recombinant antibody fragments in four different E. coli strains
This article is a report on the comparison of ANN and RSM in statistical optimization of fermentation conditions of E. coli for the soluble production of recombinant scFv.
In this report, Neural Designer was used for training and optimizing the artificial neural network with input data from densitometry analysis, and the output is the soluble expression of antiEpEX-scFv.
Based on the results, they concluded that three terms: “post-induction time”, “concentration of inducer” and “different strains” have a linear significant impact where “post-induction time” is the most important parameter.
- Hashemi, A., Basafa, M., & Behravan, A. (2022). Machine learning modeling for solubility prediction of recombinant antibody fragments in four different E. coli strains. Scientific reports, 12(1), 1-11.
Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations
The performance of epoxy systems varies significantly depending on the composition of the base resin and curing agent. However, there are limitations in exploring numerous formulations of epoxy resins to optimize adhesive properties because of the expense and time-consuming nature of the trial-and-error process. Herein, molecular dynamics (MD) simulations and machine learning (ML) methods were used to overcome these challenges and predict the adhesive properties of epoxy resin.
Datasets for diverse epoxy adhesive formulations were constructed by considering the degree of crosslinking, free volume, cohesive energy density, modulus, and glass transition temperature. ‘The researcher used neural Designer’ to train the model using these datasets and compare all the models developed with the mean squared error. Also, it is used to generate various statistical results, plots, and charts.
- Choi, J., Kang, H., Lee, J. H., Kwon, S. H., & Lee, S. G. (2022). Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations. Nanomaterials, 12(14), 2353.
Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat near ambient temperature…
The input variables are THin Inlet temperature of hot water, TMin Inlet temperature of recooling water, TLin Inlet temperature of ice-water, VH Volume flow rate of hot water, VL Volume flow rate of ice-water, VM Volume flow rate of recooling water. The target variables are THout Outlet temperature of hot water and TLout Outlet temperature of ice water. First, the model is trained with Neural Designer ‘ and later, a statistical error analysis is performed with great results with a correlation of 0.91 for TLout and 0.89 for THout.
The model is an easy-to-use and powerful optimization tool for the adsorption chiller, integrated into a combined multigeneration system. Moreover, with model deployment features, this model could be used in different languages and programs like Python or C.
- Krzywanski, J., Sztekler, K., Bugaj, M., Kalawa, W., Grabowska, K., Chaja, P. R., … & Bykuć, S. (2021). Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks.
Bulletin of the Polish Academy of Sciences. Technical Sciences, 69(3).
N,S co–doped biocarbon for supercapacitor application: Effect of electrolytes concentration and modeling with artificial neural network
The world energy crisis has been ignited by overconsumption and undue pressure on fossil fuel supplies. As a result, researchers have directed concerted efforts on energy storage devices to curb this hazard. As a result, supercapacitors have attracted interest in recent years as competitive and robust energy storage systems.
In this case, the objective is to predict different electric properties of nitrogen and sulfur co-doped chicken bone-derived biocarbon: Capacitance and energy density. Input variables are Sulfur-doping, Nitrogen-doping Electrolyte concentration, Electrolyte type, and S/N-co-doping.
The researcher used ‘Neural Designer’ for choosing and selecting the model parameters, i.e., the number of layers and neurons. As a result, this model achieves an R2 of 0.964 and an average desirability function of 0.96.
- Oladipo, A. A. (2021). N, S co–doped biocarbon for supercapacitor application: Effect of electrolytes concentration and modeling with artificial neural network. Materials Chemistry and Physics, 260, 124129.
Investigations into succinic acid fermentation
Succinic acid (SA) is an essential chemical intermediate from which fine chemicals ( e.g., detergents), additives (for pharmaceuticals, food (taste), plant growth stimulants), as well as other essential intermediates (maleic anhydride, succinimide, 2-pyrrolidinone, dimethyl succinate), can be manufactured. A neural network for modeling and describing how factors influence SA production was established based on different fermentations.
Fermentation data were combined into a single MS Excel spreadsheet and exported to a tab-delimited text file, which could be imported into ‘Neural Designer software. 9 variables, i.e., 7 inputs (time, lactic acid, acetic acid, propionic acid, glycerol, ethanol, total sugar) and 2 outputs (succinic acid, CDW), were applied to 58 fermentation samples. It was also used ‘Neural Designer’ to create and improve the neural network architecture with the order selection algorithm and the algorithm “growing inputs”.
In this case, ‘Neural Designer’ was a key part of the study, because it provides a simple and intuitive way to model this problem and draw conclusions.
- Németh, Á. (2019). Investigations Into Succinic Acid Fermentation. Hungarian Journal of Industry and Chemistry, 1-4.
Modeling resilient modulus of fine-grained materials using different statistical techniques
The main objective of this study is to use multiple linear regression (MLR), nonlinear regression, and backpropagation artificial neural network algorithms to develop models to predict the resilient modulus of fine-grained materials based on 3709 soil samples collected from the Long-Term Pavement Performance (LTPP) website.
The critical input parameters selected for this study are: the confining pressure, nominal maximum axial stress, percent of silt, Liquid Limit, Plasticity Index, percent passing number 200 sieve, maximum dry density, percent of clay, optimum moisture content, and laboratory-determined resilient modulus.
In this case, the best approximation method is the Artificial Neural Networks simulated with the help of Neural Designer software, and it implemented algorithms such as the order and input selection methods. Furthermore, the researcher uses the Directional Output utility to analyze the influence of different parameters in the resilient modulus.
- Khasawneh, M. A., & Al-jamal, N. F. (2019). Modeling resilient modulus of fine-grained materials using different statistical techniques. Transportation Geotechnics, 21, 100263.
Prediction of the Onset of Diabetes Using Artificial Neural Network and Pima Indians Diabetes Dataset
When a human body cannot respond to the insulin properly and/or produce the required amount of insulin to regulate glucose, the human body is suffering from Diabetes.
For this purpose, a Three-Layered Artificial Neural Network and Pima Indians Diabetes dataset are used. In this Neural Network based prediction model, a logistic-activation function for activation of neurons and the Quasi-Newton method is used as the algorithm for the training.
All of these features, like graphic visualization and statistical analysis used in the paper, are included in Neural Designer by default.
- Lakhwani, K., Bhargava, S., Hiran, K. K., Bundele, M. M., & Somwanshi, D. (2020, December). Prediction of the onset of diabetes using artificial neural network and pima indians diabetes dataset. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE.
A Novel Integration of Hyper-spectral Imaging and Neural Networks to Process Waste Electrical and Electronic Plastics
This study introduced a technique combining hyperspectral imaging technology and a neural networks-based algorithm to identify and separate different types of e-waste plastics (e-plastics). Although recent technological developments in computing power allow for handling big data relatively reasonably, the model must utilize a manageable number of neurons to perform real-time sorting applications for plastic recycling.
The neural network was simulated and trained in Neural Designer before its implementation because this software is based on the C++ library OpenNN.
Their results have shown that with the five spectral bins, and a neural network with five input neurons, four hidden neurons, and three output neurons, they could classify three different types of plastic at a conveyor belt speed of 1 m/s, with 99% accuracy.
- Tehrani, A., & Karbasi, H. (2017, November). A novel integration of hyper-spectral imaging and neural networks to process waste electrical and electronic plastics. In 2017 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-5). IEEE.
Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites
The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc.). Therefore, the statistical optimization of the critical design factors has become extremely important to minimize the experimental runs and the cost involved.
In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM) and machine learning techniques. The researcher used artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be significant factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA).
In this case, Neural Designer is responsible for creating and modeling the neural network and then using the genetic algorithm to optimize it. The researcher concluded that this was the best method to predict the desired features.
- Mairpady, A.; Mourad, A.-H.I.; Mozumder, M.S. Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites. Polymers 2021, 13, 3100.