Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, face recognition. The overall performance of deep mainly depends on the number parameters used, structure networks, compatibility transfer functions. However, an inappropriate design can cause a reduction networks. A novel framework, which primarily integrates Taguchi Method to based system without considering modify network, is...

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor earthquakes. Several studies have been conducted across globe confirm correlation of emission dynamics and earthquakes, concluded that witness anomalous behaviour before occurrences several This behavior can help construct better prediction model earthquake forecasting. paper aims at employing different ensemble individual machine learning methods on real time...

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method efficiently fill in missing or irregularly sampled data. It applies data completely at random (MCAR), not (MNAR), and (MAR). IBFI utilizes the iteratively imputes values using base algorithm. For this work, tested on soil radon gas concentration (SRGC) XGBoost used as algorithm are simulated R for different missingness scenarios. based physically meaningful assumption...

Depth of anesthesia is a matter great importance in surgery. Determination depth time consuming and difficult task carried out by experts. This study aims to decide method that can classify EEG data automatically with high accuracy and, so will help the experts for determination process. consists three stages: feature extraction signals, selection, classification. In stage, 41 parameters are obtained. Feature selection stage important eliminate redundant attributes improve prediction...

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and post-processing system consisting of linear model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive high-level features are extracted by using first auto-encoder which is wired to second auto-encoder, followed Softmax function layer classify obtained from layer. The two classifier stacked in order be trained supervised approach well-known backpropagation algorithm...

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series data. The data have been collected spanning over fourteen(14) months period, which included four seismic events, and used for experimentation. (IBFI) has experimented obtained results are found more efficient missing patterns investigated time...

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped V2X capability in the total number (called penetration rate PR) is still low, thus based TSC systems need supported by some other mechanisms. PR major factor that affects quality process along evaluation interval. Quality each direction function overall an intersection. Hence, should follow Computational intelligence, more...

A sparse and low-rank matrix decomposition-based method is proposed for anomaly detection in hyperspectral data. High-dimensional data are decomposed into matrices representing background anomalies, respectively. The problem of the decomposition process defined from dictionary learning point view. Therefore, our way obtaining these differs previous studies. It aims to find a correct partition separate pixels background. After decomposition, Mahalanobis distance applied part get locations....

Many web-based attacks have been studied to understand how web hackers behave, but site defacement (malicious content manipulations of victim sites) and defacers' behaviors received less attention from researchers. This paper fills this research gap via a computational data-driven analysis public database defacers activities 96 selected who were active on Twitter. We conducted comprehensive the data: an friendship graph with 10,360 nodes, sentiments related attack patterns, topical modelling...

Abstract In recent years, the rapid development in information technologies appears form of digitalization, all processes health domain. Among state‐of‐art, virtual reality, artificial intelligence, and blockchain are among most mentioned. addition, exponential increase data kept electronic environment causes traditional central applications to face new challenges. The important these accountability, transparency, security, cost, time efficiency. this study, a model based on has been...

The process of object detection utilizing deep learning is one the most important applications and computer vision techniques, where can learn image features in normal weather conditions different rain conditions. Therefore, a convolutional neural network (DCNN) has become more for detection. Rain common maj or factor degrading quality decreasing reliability. main aim this work to remove streaks get high reliability decrease error rate, (light, medium heavy). Firstly, images improved removed...

In this paper, we studied the relationship between Vehicle Total Time (VTT) and traffic conditions for signalized intersection. VTT is duration that a vehicle remains associated with road side unit RSU through group leader. As travels along an intersection encounters different degrees of delay (i.e. conditions), value varies accordingly. Intuitively, higher indicates worse degree condition. This study, investigates ways to estimate condition based on measurements taken from Everything V2X...