autoencoder

Identifying hazards from human error is critical for industrial safety since dangerous and reckless industrial worker actions, as well as a lack of measures, are directly accountable for human-caused problems. Lack of sleep, poor nutrition, physical deformities, and weariness are some of the key factors that contribute to these risky and reckless behaviors that might put a person in a perilous scenario. This scenario causes discomfort, worry, despair, cardiovascular disease, a rapid heart rate, and a slew of other undesirable outcomes. As a result, it would be advantageous to recognize people's mental states in the future in order to provide better care for them. Researchers have been studying electroencephalogram (EEG) signals to determine a person's stress level at work in recent years. A full feature analysis from domains is necessary to develop a successful machine learning model using electroencephalogram (EEG) inputs. By analyzing EEG data, a time-frequency based hybrid bag of features is designed in this research to determine human stress dependent on their sex. This collection of characteristics includes features from two types of assessments: time-domain statistical analysis and frequency-domain wavelet-based feature assessment. The suggested two layered autoencoder based neural networks (AENN) are then used to identify the stress level using a hybrid bag of features. The experiment uses the DEAP dataset, which is freely available. The proposed method has a male accuracy of 77.09% and a female accuracy of 80.93%.

Accurate traffic classification is important for various network activities such as accurate network management and proper resource utilization. Port-based approaches, deep packet inspection, and machine learning are widely used techniques for classifying and analyzing network traffic flows. Most classification methods are suitable for small-scale datasets and cannot achieve a high classification accuracy owing to their shallow learning structure and limited learning ability. The emergence of deep learning technology and software-driven networks has enabled the application of classification methods for processing large-scale data.In this study, a two-step classification method based on deep learning algorithms is presented, which can achieve high classification accuracy without manually selecting and extracting features. In the proposed method, an Autoencoder was used to extract features and remove unnecessary and redundant features. In the second step, the proposed method uses the features extracted by the autoencoder from a hybrid deep-learning model based on the CNN and LSTM algorithms to classify network traffic.To evaluate the proposed method, the results of the proposed two-stage hybrid method is compared with comparative algorithms including decision tree, Naïve Bayes, random forest. The proposed combined CNN+LSTM method obtains the best results by obtaining values of 0.997, 0.972, 0.959, and 0.964, respectively, for the evaluation criteria of, accuracy, precision, recall, and F1 score.The proposed method is a practical and operational method with high accuracy, which can be applied in the real world and used in the detection of security anomalies in networks using traffic classification and network data.