Accurate prediction of grain boundary structures and energetics in CdTe: A machine-learning potential approach

Cloud Cover ◽

Feed Forward Neural Network ◽

Proposed Model ◽

Wide Range ◽

New Methodologies ◽

Complex Relationships ◽

Rainfall is one of the major livelihood of this world. Each and every organism in this universe need some of water to order to survive in its own living conditions. As rainfall is the main source of water and its need to agriculture is inevitable, there arises a necessity to analyze the pattern of the rainfall. The main aim of our paper is to predict the rainfall considering various factors like temperature, pressure, cloud cover, wind speed, pollution and precipitation. There are various ideas and new methodologies proposed in order to predict rainfall. But our proposed concept is based on machine learning because of its wide range of development and preferability nowadays. Among the various technologies built in Machine Learning (ML), Feed Forward Neural Network (FFNN) which is the simplest form of Artificial Neural Network (ANN) is preferred because this model learns the complex relationships among the various input parameters and helps to model them easily. Rainfall in our proposed model is predicted using different parameters influencing the rainfall along with their combinations and patterns. The experimental results depicts that the proposed model based on FFNN indicates suitable accuracy.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1343 ◽

2009 ◽

Vol 4 (1) ◽

Cited By ~ 2

Author(s):

Sandip K Lahiri ◽

Kartik Chandra Ghanta

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Operating Conditions ◽

Misclassification Error ◽

Wide Range ◽

Artificial Neural ◽

Pipeline Design ◽

Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Applications of Artificial Neural Networks for Nonlinear Data - Advances in Computational Intelligence and Robotics ◽

Fundamental Categories of Artificial Neural Networks

10.4018/978-1-7998-4042-8.ch003 ◽

2021 ◽

pp. 30-64

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch001 ◽

2022 ◽

pp. 1-30

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Advances in Medical Diagnosis, Treatment, and Care - Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder ◽

ML-PASD

10.4018/978-1-7998-7460-7.ch006 ◽

2021 ◽

pp. 82-93

Author(s):

Vishal Jagota ◽

Vinay Bhatia ◽

Luis Vives ◽

Arun B. Prasad

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Autism Spectrum Disorder ◽

Early Stage ◽

Age Groups ◽

Autism Spectrum ◽

Screening Procedures ◽

Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.

Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine ◽

Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors

10.1115/gt2020-15062 ◽

2020 ◽

Author(s):

Suleiman M. Suleiman ◽

Yi-Guang Li

Keyword(s):

Neural Network ◽

Field Measurements ◽

Discharge Temperature ◽

Novel Approach ◽

Wide Range ◽

Artificial Neural ◽

Compressor Map ◽

Map Data

Abstract This paper presents the development of an artificial neural network (ANN) Gas Path Diagnostics (GPD) technique applied to pipeline compression system for fault detection and quantification. The work detailed the various degradation mechanisms and the effect of such degradations on the performance of natural gas compressors. The data used in demonstrating the ANN diagnostics is so derived using an advanced thermodynamic performance simulation model of integrated pipeline and compressor systems, which has embedded empirical compressor map data and pipeline resistance model. Implantation of faults within the model is in such a way to account for faults degradations caused by fouling, erosion and corrosion, of various degrees of severities, to obtain wide range of corresponding simulated “true” measurements. In order to account for uncertainties normally encountered in field measurements, Gaussian noise distribution was combined with simulated true measurements, which depends on the instrument’s tolerances. Furthermore, since judicious measurements selection are crucial in ensuring flawless GPD predictions, a sensitivity and correlation analysis of the available measurements revealed that discharge temperature, rotational speed and torque are the most effective measurements for the diagnostics with acceptable degrees of accuracies. The measurements observability technique is a novel approach in pipeline compressor diagnostics. Analytical case studies of the developed method show that, a selected ANN architecture can detect and quantify faults related to degradation in efficiency and flow capacities in the presence of instrument error, varied operational and environmental conditions.

Probabilistic estimation of software development effort techniques using machine learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.12233 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 1085

Author(s):

Dr P. Vidya Sagar ◽

Dr Nageswara Rao Moparthi ◽

Venkata Naresh Mandhala

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Support Vector ◽

Development Effort ◽

Probabilistic Estimation ◽

Software Development Effort ◽

High Level

Precisely assessing programming exertion is likely the greatest test confronting for programming engineers. Assessments done at the prop-osition arrange has high level of incorrectness, where prerequisites for the degree are not characterized to the most reduced subtle elements, but rather as the venture advances and necessities are explained, exactness and certainty on appraise increments. It is vital to pick the correct programming exertion estimation systems for the forecast of programming exertion. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been utilized on guarantee dataset for forecast of programming exertion in this article.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

Journal of Nanotechnology in Engineering and Medicine ◽

10.1115/1.4003500 ◽

2011 ◽

Vol 2 (1) ◽

Cited By ~ 5

Author(s):

Hadi Salehi ◽

Mosayyeb Amiri ◽

Morteza Esfandyari

Keyword(s):

Neural Network ◽

Experimental Data ◽

Energy Gain ◽

Operating Conditions ◽

Maximum Relative Error ◽

Ann Model ◽

Wide Range ◽

Artificial Neural ◽

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

Artificial Neural Network Algorithms for 3D Printing

Materials ◽

10.3390/ma14010163 ◽

2020 ◽

Vol 14 (1) ◽

pp. 163

Author(s):

Muhammad Arif Mahmood ◽

Anita Ioana Visan ◽

Carmen Ristoscu ◽

Ion N. Mihailescu

Keyword(s):

Neural Network ◽

Machine Learning ◽

3D Printing ◽

Process Parameters ◽

Optimization Methods ◽

Physical Models ◽

Network Algorithms ◽

Artificial Neural ◽

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

Prediction of Graft Survival Using Artificial Neural Network (ANN), and Bayesian Belief Network (BBN): A Comparative Study

Asia Pacific Journal of Health Management ◽

10.24083/apjhm.v16i3.975 ◽

2021 ◽

Vol 16 (3) ◽

pp. 29-35

Author(s):

C. Theophilus Dhyankumar ◽

C. Joe Arun ◽

M. Rajmohan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Decision Making ◽

Graft Survival ◽

Bayesian Belief Network ◽

Post Transplant ◽

Probability Of Survival ◽

Objective: The aim of this study was to predict graft survival using machine learning prediction techniques and the involved decision making. Design: Prediction of graft survival post-transplant using machine learning algorithms like Artificial Neural Network (ANN) (Single and Multi-layer networks), and Bayesian Belief Network (BBN). Setting: Recipient and donor with characteristics of age, sex and Glomerular Filtration Rate (GFR) and the follow-up of probability of survival one year after transplantation (n=40). Main outcome measures: The Data include simulation from donor, recipient characteristics of single centre with factors age, sex, GFR and probability of survival collected particularly with the follow-up after the first year of transplant. Results: The ANN and BBN were modelled in Python. The probability of survival post-transplant is predicted, and accuracy measured using Root Mean Square Error (RMSE). The results for the methods were compared and efficacy and ease of use are discussed. Conclusion: The decision making in the organ transplantation involving the patients and doctors consists of mainly involve improving the graft survival and hence prediction becomes important. The developed models can be used to predict the transplant and aid as decision support system for decision regarding matching and allocation.