Biblio

Recently, social networks have become more popular owing to the capability of connecting people globally and sharing videos, images and various types of data. A major security issue in social media is the existence of fake accounts. It is a phenomenon that has fake accounts that can be frequently utilized by mischievous users and entities, which falsify, distribute, and duplicate fake news and publicity. As the fake news resulted in serious consequences, numerous research works have focused on the design of automated fake accounts and fake news detection models. In this aspect, this study designs a hyperparameter tuned deep learning based automated fake news detection (HDL-FND) technique. The presented HDL-FND technique accomplishes the effective detection and classification of fake news. Besides, the HDLFND process encompasses a three stage process namely preprocessing, feature extraction, and Bi-Directional Long Short Term Memory (BiLSTM) based classification. The correct way of demonstrating the promising performance of the HDL-FND technique, a sequence of replications were performed on the available Kaggle dataset. The investigational outcomes produce improved performance of the HDL-FND technique in excess of the recent approaches in terms of diverse measures.

Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.

Though the GNSS receiver baseband signal processing realizes more precise estimation by using Kalman Filter, traditional KF-based tracking loops estimate code phase and carrier frequency simultaneously by a single filter. In this case, the error of code phase estimate can affect the carrier frequency tracking loop, which is vulnerable than code tracking loop. This paper presents a tracking architecture based on dual filter. Filters can performing code locking and carrier tracking respectively, hence, the whole tracking loop ultimately avoid carrier tracking being subjected to code tracking errors. The control system is derived according to the mathematical expression of the Kalman system. Based on this model, the transfer function and equivalent noise bandwidth are derived in detail. As a result, the relationship between equivalent noise bandwidth and Kalman gain is presented. Owing to this relationship, the equivalent noise bandwidth for a well-designed tracking loop can adjust automatically with the change of environments. Finally, simulation and performance analysis for this novel architecture are presented. The simulation results show that dual Kalman filters can restrain phase noise more effectively than the loop filter of the classical GNSS tracking channel, therefore this whole system seems more suitable to working in harsh environments.