Using data mining techniques for bike sharing demand prediction in metropolitan city

Sathishkumar, V E; Park, Jang-Woo; Cho, Yongyun

doi:10.1016/j.comcom.2020.02.007

Cited by 113 publications

(51 citation statements)

References 18 publications

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“…The dataset is a compilation of a year's worth of data from the Seoul Public Data Park website. Each datapoint describes the number of bikes rented in the city for a single hour of a particular day, along with a number of meteorological and date-specific details that may have correlated with the demand for bikes [19]. This dataset is available on the UC Irvine Machine Learning Repository.…”

Section: Seoul Bike Sharing Demand Experimentsmentioning

confidence: 99%

Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise

Thamsanqa¹,

Deventer²,

Bosman³

2023

Preprint

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In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data. For example, the frequently used mean squared error (MSE) loss assumes a Gaussian noise distribution. The choice of loss function during training and testing affects the performance of artificial neural networks (ANNs). It is known that MSE may yield substandard performance in the presence of outliers. The Cauchy loss function (CLF) assumes a Cauchy noise distribution, and is therefore potentially better suited for data with outliers. This papers aims to determine the extent of robustness and generalisability of the CLF as compared to MSE. CLF and MSE are assessed on a few handcrafted regression problems, and a real-world regression problem with artificially simulated outliers, in the context of ANN training. CLF yielded results that were either comparable to or better than the results yielded by MSE, with a few notable exceptions.

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Section: Seoul Bike Sharing Demand Experimentsmentioning

confidence: 99%

Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise

Thamsanqa¹,

Deventer²,

Bosman³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, the models received 94.10 percent for weighted recalls, accuracy, and F1-scores, and 94.10 percent for weighted recalls, accuracy, and F1scores. Table 7 shows a comparison of the model with and without feature selection [37][38][39][40][41][42][43][44][45][46][47].…”

Section: Description Of K-fold Cross-validationmentioning

confidence: 99%

Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information

Devi

Karthikeyan

Moorthy

et al. 2022

Mathematical Problems in Engineering

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Microarray data are becoming a more essential source of gene expression data for interpretation and analysis. To improve the detection accuracy of tumors, the researchers try to use the lowest feasible collection of the most gene expression studies, and relevant gene expression patterns are found. The purpose of this article is to use a data mining strategy and an optimized feature selection method focused on a limited dense tree forest classifier to evaluate and forecast colon cancer data. More specifically, merging the “gain information” and “Grey wolf optimization” was incorporated as a feature selection approach into the random forest classifier, to improve the prediction model’s accuracy. Our suggested technique can decrease the load of high-dimensional data, and it allows quicker computations. In this research, we provided a comparison of the analysis model with feature selection accuracy over model analysis without feature selection accuracy. The extensive experimental findings have shown that the suggested method with selecting features is beneficial, outperforming the good classification performances.

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“…Deep neural networks, notably the CNNs, are frequently employed in image classification tasks and have demonstrated substantial performance since 2012 [21][22][23][24]. CNN's study on medical image categorization has produced results that are comparable to those of human experts.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images

Malliga¹,

Kumar

Sathishkumar

et al. 2022

Computational Intelligence and Neuroscience

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Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.

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Using data mining techniques for bike sharing demand prediction in metropolitan city

Cited by 113 publications

References 18 publications

Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise

Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise

Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information

Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images

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