Artificial Neural Network In Order Research Articles

Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States.

The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States. Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets. The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of+24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at-775 and-575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at-64 and-180 cases per year in 2020 and in 2030, respectively, for obesity, and-173 and-21 cases per year in 2020 and in 2030, respectively, for smoking. Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

Identifikasi Posisi Robot Quadpod pada Arena Pertandingan Menggunakan Jaringan Syaraf Tiruan - Algoritma Backpropagation

The Indonesian Robot Contest (KRI) is an event to develop creativity and enrich science and technology in the field of robotics. In KRI, there are several branch categories, one of which is the KRPAI (Indonesian Fire Fighting Robot Contest) category. In the KRPAI category competition, the robot aims to find a room where there is a fire, then the robot will extinguish the fire in the room, and return to the starting point. This requires the robot to know its own actual position so that it can return to the starting point. This can be solved by adding artificial intelligence (Artificial Intelligence) artificial neural networks. One of the neural network models used for the learning process is backpropagation. It should be noted that in artificial intelligence systems, artificial neural networks in order to work optimally in identifying robot positions, it is necessary to do a learning process (learning) first in order to get the optimal weight value. The learning system is carried out by entering 280 position data samples that present 28 positions. Learning data is obtained by reading the distance between the robot and the maze wall and the direction facing the robot. To test the artificial neural network system, a running process is carried out to determine the position of the robot. From 140 trial data conducted, the artificial neural network was able to recognize the position of the robot correctly with an accuracy of 92.85%. The simulation results are expected to be applied to the actual quadpod robot.

Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders

The optimal outputs of pyrophyllite mechano-chemical activation in an ultra-centrifugal mill performing under different technological conditions were determined by analytical modeling and verified via Artificial Neural Network in order to be employed in the production of cement-based binders. Cluster Analysis and Principal Component Analysis were utilized in assessment of the effect of activation process parameters on the activated pyrophyllite quality. Artificial Neural Network which performed with high prediction accuracy, i.e. 0.914 during the training period, was sufficient for precise prediction of activated pyrophyllite quality in a wide range of processing parameters. The probability of utilization of observed activation products was estimated through interrelation of technological parameters (mesh size sieve, activation period, specific energy consumption) and acquired characteristics of pyrophyllite (grain diameter, specific surface area). The optimal products singled out from each activation sequence were used as mineral additives in the mix-designs of four cement binders (cement replacement portion was 30%). Influence of activated pyrophyllite additions on the cement chemistry, mineral phase compositions and microstructures of the cement binders were monitored by instrumental techniques (DTA/TGA, XRD, SEM). Activated pyrophyllite showed characteristics of pozzolana as it slightly accelerated early stages of hydration, decreased cement hydration energy and increased the quantity of cement mineral alite at later hydration stages. Micron-sized crystalline foila characteristic for mechanically activated pyrophyllite formed micro-reinforcement within cement binder microstructure.

Calculation of parameters for altering the ship’s course at sea using Artificial Neural Network (ANN)

During ship manoeuvring and course alteration, the trajectory of ship or the time of ship’s turning which remarkably affect safety and effectiveness of navigation depends on ship’s factor, meteorological factor and control factor. Simulation commonly is applied for methods used for calculating the ship’s trajectory. However, these computed results which are only applied for simulated cases or for certain external affected conditions are not reliable enough for navigation officers to predict the ship’s turning index in practical situations. In this article, authors propose the model for calculating the ship’s course alteration by applying artificial neural network in order to assist navigation officers making decision of manoeuvring ship safely and effectively, especially in emergency situations.

Supervised machine learning for audio emotion recognition

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.

Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network

Drilling depleted and ultra-deep gas reservoirs required special drilling fluids which should be capable of bridging along the walls of the well and withstands a high differential pressure. High-overbalanced water-based drilling fluid (HOBWBDF) is one of the most common drilling fluid used to drill the ultra-deep gas wells. Drilling fluids properties such as yield point (YP), plastic viscosity (PV), apparent viscosity (AV), flow behavior index (n) and consistency index (k) are very vital inputs in managing rig hydraulics, margins of surge and swab pressure, equivalent circulation density (ECD) and hole cleaning. There is a need to develop a new technique to estimate the rheological properties in real-time using the frequent measurements of mud density and Marsh funnel viscosity, which are carried out more frequently (every 15–20 min).The main goal beyond this work is to build novel empirical models that are capable of predicting the above-mentioned rheological properties of high over-balanced water-based drilling fluid (HOBWBDF) in real-time for the first time. The models are built based on only two inputs which are the mud weight from the mud balance test and Marsh funnel viscosity measured by the Marsh funnel test. More than 1200 field measurements of rheological properties were used to feed an artificial neural network in order to develop new novel empirical models.The developed models using the ANN technique showed high accuracy for predicting the different drilling fluid rheological properties. The obtained results showed a maximum average absolute percentage error (AAPE) less than 8% with a correlation coefficient higher than 93%. The main advantages of these models are their inexpensiveness as there is no need for additional equipment to be added to the rig site. Moreover, these models would greatly help the drilling engineers to manage the ECD, surge and swab pressure and hole cleaning which in return will be reflected positively on the drilling performance.

Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks

Introduction: Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.

Managing a greenhouse complex using the synergetic approach and neural networks

The paper considers the use of artificial neural networks in order to synthesize intelligent systems governed by a synergetic control law. It has been shown that so far all the studied objects and, therefore, control laws, have been considered linear, or have been treated to reduce them to such, thereby compromising their certain features. However, as evidenced by practice, actual objects are mostly nonlinear. Consideration of such objects with an attempt of their linearization leads to that the important characteristics of the entire process are lost. A greenhouse complex is mostly composed of such nonlinear objects of control. A greenhouse, as well as each process separately, are not exception.We have proposed basic provisions to the synergistic approach related to the systems synthesis task. The synergistic synthesis of control law has been shown for a greenhouse complex under conditions of non-controlling changes in the technological parameters and external disturbances. The applied mathematical apparatus of fuzzy logic enables the implementation of fuzzy control. It manifests itself particularly positively under conditions when the processes are difficult to analyze by using conventional quantitative methods. As well as when the acquired information about the object is substandard, inaccurate, or ambiguous. This is exactly the type of information received for analysis and its subsequent use when growing vegetables at greenhouse complexes. We have proposed an algorithm to synthesize a neuro-network controller for a greenhouse complex based on the predefined synergetic control law. The algorithm is based on the performance of the synergistic controller that simulates values for temperature and humidity from an artificial neural network following our training it. A feature of the proposed integrated approach to the synthesis of an intelligent control system for a greenhouse complex is a combination of the principle of unification of the processes of self-organization and training a neural network at a preliminary stage. Such a combination ensures further stable functioning of the system aimed to intelligently control the cultivation of vegetable produce.

TAPE MODELING OF EUCALYPTUS STEM IN CROP-LIVESTOCK-FORESTRY INTEGRATION SYSTEMS

This paper aims to evaluate and compare the mixed effects modeling and artificial neural networks in order to estimate the taper of eucalyptus in integrated Crop-Livestock-Forestry (iCLF) systems. The data were collected in an experimental area of iCLF, implanted by the Brazilian Company of Farming Research – EMBRAPA Agrossilvipastoril, located in the municipality of Sinop, Mato Grosso State, Brazil. To reach the proposed aim, 165 trees with 51 months of age were scaled for the taper modeling with mixed effects models and artificial neural networks. The performance of these techniques was evaluated through precision measurements and graphical analysis. Mixed effects modeling and artificial neural networks are efficient and recommended in the estimative of taper of eucalyptus in integrated Crop-Livestock-Forestry system; however, despite both evaluated techniques present accurate results in predicting the taper of the sampled trees, the artificial neural network predicts values with greater precision than the modeling of mixed effects.

Critical Success Factors of Sustainable Manufacturing and Procurement

All countries are taking action and making plans for sustainable manufacturing and procurement so that the coming generations could not blame them for exhausting all available resources. In this study, significant variables of sustainable manufacturing for Indian manufacturing industries have been identified. The result of present study revealed that agile manufacturing has the highest impact on sustainable manufacturing followed by lean manufacturing and sustainable procurement and other variables. The outcome of results suggests that the government should be strict towards the implementation of sustainability norms in manufacturing industries and financial assistance should be provided by the government for better implementation of sustainability. A research framework has been developed and a regression equation for sustainable manufacturing has been developed in present paper using a stepwise linear regression analysis. The present article also explores the suitability of artificial neural network in order to identify the importance of studied variables.

Prediction of Bankruptcy using Big Data Analytics based on Fuzzy c-means Algorithm

<p class="western" align="justify"><span style="color: #000000;"><span style="font-family: 'Times new roman', serif;"><span style="font-size: small;"><span>This paper has suggested an optimization approach of the cluster-based sampling using Fuzzy c means algorithm to the classifier in order to select the most appropriate instances of bankruptcy. This method was examined with the help of a clustering method and GA based artificial neural network in order to solve the existing data imbalance issue. The objective of this paper is to optimise the selected design model of GA-ANN by using Fuzzy C means algorithm to predict corporate bankruptcies by considering different financial ratios of companies across several industries within the period from 1994 to 2014. Effectiveness of this method was proved by comparing its accuracy rate with the results of existing method. From the performance result the accuracy rate of this method was found to be 78.2% and misclassification rate to be 0.2178.</span></span></span></span></p>

Error correction in quantum cryptography based on artificial neural networks

Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problems remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent; therefore, the strings are similar and also users’ neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.

The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP

This paper employs artificial intelligence methods in order to create a function for compressive strength of the boroaluminosilicate geopolymers based on mixture proportion variables. Boroaluminosilicate geopolymers (BASGs), a group of boron-based alkali-activated materials, not only minimise the carbon footprint in the construction industry but also decrease the consumption of energy and natural resources. Australian fly ash and iron making slag are activated in sodium and boron-based alkaline medium in order to produce the geopolymer binders. The current study employs artificial neural network in order to classify the collected data into train, test, and validation followed by genetic programming for developing a function to approximate the compressive strength of BASGs. The independent variables comprise the percentage of fly ash and slag as well as ratios of boron, silicon, and sodium ions in the alkaline solution. The performance of each method is assessed by the acquired regression and the error parameters. The obtained results show that the percent of silicon and boron ions, with positive direct correlation and the largest power in the function respectively, have the most significant effects on the compressive strength of BASG. The assessment factors, including R-squared 0.95 and root-mean-square error 0.07 in the testing data, indicate that the model explains all the variability of the response data around its mean. It implies a high level of accuracy and reliability for the model.

Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks.

The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA) <5/≥5 and Clinical stage I–II/III–IV. The performance of each model was evaluated by predictive accuracy (ACC), the area under the receiver operating characteristic curve (AUC) and a 10-fold cross-validation test. The ACC and AUC of the Cancer/Normal, M0/M1, CEA and Clinical stage models were 100%, 1.000; 87.14%, 0.670; 100%, 1.000; and 100%, 1.000, respectively. The 10-fold cross-validation test of the ACC values and sensitivity for each test were 93.75–99.39%, 1.0000; 80.58–88.24%, 0.9286–1.0000; 67.21–92.31%, 0.7091–1.0000; and 59.13–68.85%, 0.6017–0.6585, respectively. The diagnosis models developed in the current study combined gene expression profiling data and artificial intelligence algorithms to create tools for improved diagnosis of CRC.

Experimental investigations on wear and friction behaviour of SiC@r-GO reinforced Mg matrix composites produced through solvent-based powder metallurgy

In the present study, wear and friction behaviour of Magnesium(Mg) Metal Matrix Composite(MMC) reinforced with Silicon carbide(SiC) doped reduced graphene oxide (r-GO) nanosheets is carried over. In addition, a mathematical model is developed to predict the influence of various control factors of the Mg composites fabricated through Solvent-based powder metallurgy process. Herein SiC is doped with varying wt. % (10, 20, 30) into r-GO nanosheets and its effect over dry sliding wear is studied at constant control parameters like that of load (10N), sliding distance (1000m) and sliding velocity (1 m/s). Optimal parameter for specific wear rate (SWR) is attained by Taguchi method and the mathematical model was developed using Artificial Neural Network in order to understand the wear behaviour of developed MMC under varying parametric condition. Analysis of variance result reveals that wt.% of r-GO have major influence on SWR and sliding velocity have least effect. Occurrence of delamination wear could also be notified over the worn out surface.

Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method

Land cover classification analysis from satellite imagery methods are important because they are the basis for characterizing surface conditions and evolution, supporting the management and optimization of land resources, evaluating global climate and environmental changes, and facilitating sustainable regional economic and social development. In order to address these necessities, artificial neural networks have been used extensively. In addition, other methods based on computer vision are very useful to solve this task. In this paper, the authors propose an approach based on Monte Carlo method and artificial neural networks in order to classify regions of small forest reserves from drones’ images and calculate their respective areas. Next to the small forest reserve will be extended a standard rectangular tarpaulin of 250 square meters and based on this reference it will be possible to calculate the area of the forest reserve if the ground is relatively flat. The proposed approach will be compared with a method based on watershed algorithm. The automatic calculation of the forest area through images generated by drones has much practical application for environmental engineers, for example, for the calculation of environmental impact and determination of carbon loss if such forests are consequently deforested.

Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy

Abstract The developed model is an association of thermodynamic calculations for dissolution of alloys, slag formers and the deoxidation reaction in the molten steel with two artificial neural network (ANN) models trained with industrial data, to predict the molten steel temperature drop from the blowing end of the BOF until the first measurement at secondary metallurgy. To calculate the associated energy for deoxidation, an experiment was designed to set up the parameters for oxygen partitioning among deoxidants, with timed aluminum addition during teeming being the main parameter. The temperature control in the teeming stage presented a standard deviation for the error of prediction of 5.46 oC, for transportation from the rinsing station to the secondary metallurgy of 2.79 oC. The association of all calculations presented an error standard deviation of 7.49 oC. The operational validation presented superior accuracy compared with the current method for controlling the temperature, resulting in a reduction in the aluminum consumption for heating at secondary metallurgy with a potential economy of U$ 4.07 million per year for a steel shop producing 5 million tons of steel yearly. The artificial neural network model confirmed its capacity for modeling a complex multivariable process and the separation of thermodynamic calculation provides a better adaptability to different steel grades with different teeming strategies.

Prediction of Layered Thermal Conductivity Using Artificial Neural Network in Order to Have Better Design of Ground Source Heat Pump System

Ground source heat pumps (GSHPs) have been widely applied worldwide in recent years because of their high efficiency and environmental friendliness. An accurate estimation of the thermal conductivity of rock and soil layers is important in the design of GSHP systems. The distributed thermal response test (DTRT) method incorporates the standard test with a pair of fiber optic-distributed temperature sensors in the U-tube to accurately calculate the layered thermal conductivity of the rock/soil. In this work, in situ layered thermal conductivity was initially obtained by DTRT for four boreholes in the study region. A series of laboratory tests was also conducted on the rock samples obtained from drilling. Then, an artificial neural network (ANN) model was developed to predict the layered thermal conductivity on the basis of the DTRT results. The primary modeling factors were water content, density, and porosity. The results showed that the ANN models can predict the layered thermal conductivity with an absolute error of less than 0.1 W/(m·K). Finally, the trained ANN models were used to predict the layered thermal conductivity for another study region, in which only the effective thermal conductivity was measured with the thermal response test (TRT). To verify the accuracy of the prediction, the product of pipe depth and layered thermal conductivity was suggested to represent heat transfer capacity. The results showed that the discrepancies between the TRT and ANN models were 5.43% and 6.37% for two boreholes, respectively. The results prove that the proposed method can be used to determine layered thermal conductivity.

Centralized Rainfall Estimation Using Carrier to Noise of Satellite Communication Links

In this paper, we present a centralized method for real-time rainfall estimation using carrier-to-noise power ratio ( $C/N$ ) measurements from broadband satellite communication networks. The $C/N$ data of both forward link and return link are collected by the gateway station from the user terminals in the broadband satellite communication network and stored in a database. The $C/N$ for such Ka-band scenarios is impaired mainly by the rainfall. Using signal processing and machine learning techniques, we develop an algorithm for real-time rainfall estimation. Extracting relevant features from $C/N$ , we use artificial neural network in order to distinguish the rain events from dry events. We then determine the signal attenuation corresponding to the rain events and examine an empirical relationship between rainfall rate and signal attenuation. Experimental results are promising and prove the high potential of satellite communication links for real environment monitoring, particularly rainfall estimation.

Reactor pressure vessel embrittlement: Insights from neural network modelling

Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.