List of Articles autoencoder

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - An Autoencoder based Emotional Stress State Detection Approach by using Electroencephalography Signals
  Jia Uddin
  10.52547/jist.32267.11.41.24
  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 wea More

  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%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Spam Detection in Twitter by Ensemble Learning Approach
  Maryam Fasihi Mohammad Javad shayegan zahra hosieni zahra sejdeh
  Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributi More

  Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributing spam posts. This paper employs a combination of machine learning methods to identify spam at the tweet level. The proposed method utilizes a feature extraction framework in two stages. In the first stage, Stacked Autoencoder is used for feature extraction, and in the second stage, the extracted features from the last layer of Stacked Autoencoder are fed into the softmax layer for prediction. The proposed method is compared and evaluated against some popular methods on the Twitter Spam Detection corpus using accuracy, precision, recall, and F1-score metrics. The research results indicate that the proposed method achieves a detection of 78.1%. Overall, the proposed method, using the majority voting approach with a hard selection in ensemble learning, outperforms CNN, LSTM, and SCCL methods in identifying spam tweets with higher accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - A New Hybrid Method Based on Intelligent Algorithms for Intrusion Detection in SDN-IoT
  Zakaria Raeisi Fazlloah Adibnia Mahdi Yazdian
  In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networ More

  In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networks and the Internet of Things have made SDN-IoT security one of the most important concerns. On the other hand, the use of intelligent algorithms has been an opportunity that these algorithms have been able to make significant progress in various cases such as image processing and disease diagnosis. Of course, intrusion detection systems for SDN-IoT environment still face the problem of high false alarm rate and low accuracy. In this article, a new hybrid method based on intelligent algorithms is proposed. The proposed method integrates the monitoring algorithms of frequent return gate and unsupervised k-means classifier in order to obtain suitable results in the field of intrusion detection. The simulation results show that the proposed method, by using the advantages of each of the integrated algorithms and covering each other's disadvantages, has more accuracy and a lower false alarm rate than other methods such as the Hamza method. Also, the proposed method has been able to reduce the false alarm rate to 1.1% and maintain the accuracy at around 99%. Manuscript profile