Neural network implementation in Java

Jovanović, Nenad; Vasović, Bojan; Jovanović, Zoran; Cvjetković, Miloš

doi:10.5937/bizinfo2001019j

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…The training is based on the backpropagation algorithm. Rummelhart [27] proposed an algorithm that was inspired by the gradient method, which he named “backpropagation.” According to this algorithm, the output error should be returned to the previous layers, then find the influence of individual weights on the error, and determine the weight gain in all layers [16].…”

Section: System Descriptionmentioning

confidence: 99%

NNeduca: A software environment to teach artificial neural networks

Jovanović¹,

Stamenković²,

Jovanović

2023

Comp Applic In Engineering

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Artificial neural networks are an integral part of the curriculum in many undergraduate computer science programs. This paper introduces NNeduca, a simulation software system designed to teach undergraduate students the fundamental concepts of artificial neural networks. The system is written in Java and aims to provide an intuitive user interface, support for research and learning activities, and the ability to expand. This tool allows students to design and train a neural network with any architecture they desire and visualize some artificial neural network theoretical concepts such as definitions, topologies, training methods, and structure. An overview of educational software systems to aid in learning the fundamental concepts of neural networks was created based on a systematic literature review. A comparison of existing simulation systems and the developed NNeduca tool was performed in this paper. The evaluation was carried out using a newly developed model for assessing the quality of selected systems using established criteria formed as a result of a thorough examination of relevant methods of evaluating educational software. The usability evaluation results show that the NNeduca tool received the highest ratings, indicating that it can significantly improve the teaching process in artificial neural network courses.

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Section: System Descriptionmentioning

confidence: 99%

NNeduca: A software environment to teach artificial neural networks

Jovanović¹,

Stamenković²,

Jovanović

2023

Comp Applic In Engineering

Self Cite

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Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Matijošius,

Rimkus,

Gruodis

2024

Machines

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Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

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Neural network implementation in Java

Cited by 2 publications

References 2 publications

NNeduca: A software environment to teach artificial neural networks

NNeduca: A software environment to teach artificial neural networks

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

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