Dark web is the hidden region of the Internet. The anonymity it offers is of course used for rightful causes such as free speech;but it also brings in users that use it for illicit activities. The latter forms the bas...
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Driving safety is crucial for maintaining road safety, and helmet use by motorcyclists is essential for reducing serious head injuries during accidents. However, compliance with helmet use remains challenging, especia...
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Object detection is one of the most challenging problems in computer Vision. It is difficult because there are many variations between images which have the same object category. Other factors include changes in persp...
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Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lig...
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A lethal eye ailment called glaucoma causes the retina to gradually degrade over time. Although there is no complete cure, early detection helps slow the disease’s course. Early diagnosis is very rare because there a...
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Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network a...
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Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network architecture. This unit then gives an advantage to RBGU-RNN over the existing LSTM network. Firstly, given that the RBGU is just an activation unit and which do not perform any weighted operations as it should in a classical neuron unit, it has an advantage for not propagating (duplicating) error as compared to the LSTM. Secondly, due to the fact that this unit is located at the beginning of the network treatment workflow, it provides standardization to the data set, before they are run into the weighted units, which is not the case of a simple LSTM. This study then provided a theoretical and experimental comparison of the LSTM and RBGU-RNN. Indeed, using a real world call data record, precisely a survey on the end user cell network data traffic, we built up a cellular traffic prediction model. We start with ARIMA model which permit us to choose the number of time steps needed to build the RBGU-RNN prediction model that is the number of time steps needed to predict the next individual in the time series. The results show that RBGU-RNN accurately predict cellular data traffic with great success in generalization than LSTM. The R-squared statistics or determination coefficients show that 58.31 % of user traffic consumption can be explained by LSTM model, while 96.86 % of the user traffic consumption can be done by RBGU-RNN model in the training set. Likewise, in the test set, we found that 61.24 % of user traffic consumption can also be explained by LSTM model and 95.20 % can be done by RBGU-RNN. Also, the RBGU-RNN has more efficient gradient descent than the standard LSTM by analysing and experimenting the graphs given by the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the Maximum Absolute Error (MAXAE) functions over the numbe
Data visualization is the graphical and pictorial display of data. Data visualization and statistical graphics are often conceived of as latest advancement in statistics and relatively moves hand in hand. Process of c...
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We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability ...
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Lung cancer is consistently ranked as the primary cause of cancer-related fatalities worldwide. The timely identification and effective treatment of lung cancer play a pivotal role in patient survival rates. Generally...
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Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcome...
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