Performance Comparison of Multiple ANN Optimizer on IoT-enabled Sensor Fire Dataset

Sudip Suklabaidya, Indrani Das
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Abstract

In today's world, fires in homes and commercial places are a serious problem that can harm the local environment as well as jeopardize people's property and lives. This study predicts the sensor dataset gained from an integrated sensor framework with an artificial neural network. The major goal of this research was to identify a convenient way to encode input data that balanced information loss with simplicity. This paper developed an Artificial Neural Network (ANN) model and applied it to the fire dataset collected from the Integrated Sensor System (ISS). Every neuron of the model will learn and hold weights that weigh information, which provides better accuracy. To mitigate loss functions and improve accuracy, various activation functions such as Sigmoid, Relu, and optimizer Stochastic Gradient Descent (SGD), Adam, and Adamax are used in the designed model. The results demonstrated that the prediction accuracy of the ANN model with Adam as the optimizer is better than that of the other two optimizers. The findings also show that the ANN model performs well in terms of prediction accuracy and is also better suited to the sensor fire dataset
多神经网络优化器在物联网传感器火灾数据集上的性能比较
在当今世界,家庭和商业场所的火灾是一个严重的问题,它不仅会危害当地环境,还会危及人们的财产和生命。本研究利用人工神经网络预测从集成传感器框架中获得的传感器数据集。本研究的主要目标是确定一种方便的方法来编码输入数据,以简单的方式平衡信息丢失。本文建立了一种人工神经网络(ANN)模型,并将其应用于综合传感器系统(ISS)采集的火灾数据集。模型的每个神经元都将学习并持有加权信息的权重,从而提供更好的准确性。为了减轻损失函数并提高精度,在设计的模型中使用了各种激活函数,如Sigmoid, Relu和优化器随机梯度下降(SGD), Adam和Adamax。结果表明,以Adam为优化器的人工神经网络模型的预测精度优于其他两种优化器。研究结果还表明,人工神经网络模型在预测精度方面表现良好,也更适合传感器火灾数据集
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