With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models.

In recent years, wireless channel optimization technologies witnessed tremendous improvements. In this regard, research for developing wireless spectrum for accommodating a wider range of wireless devices increased. This also helped in resolving spectrum scarcity issues. Cognitive Radio (CR) is a type of wireless communication in which a transceiver can intelligently detect which communication channels are being used. To avoid interference, it instantly moves traffic into vacant channels by avoiding the occupied ones. Cognitive Radio (CR) technology showed the potential to deal with the spectrum shortage problem. The spectrum assignment is often considered as a key research challenge in Cognitive Radio Networks (CRNs). In this paper, an evolutionary optimization algorithm is proposed for channel assignment in CRNs. Evolutionary algorithms are inspired by some type of biological evolution technique. In the proposed technology we used Particle Swarm Optimization (PSO). The resulting algorithm is called differential evolution-based particle swarm optimization with the repair process (DEPSO-RP). Moreover, a repair process is introduced to remove conflicts among secondary users (SUs) to increase the spectrum in CRNs. The performance of DEPSO-RP spectrum assignment algorithm has been evaluated by extensive simulations. The proposed spectrum assignment algorithm showed better performance regarding channel assignment in comparison with other existing algorithms in the literature.

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

The advent of emerging wireless technologies has increased the demand of spectrum resources. On the other hand, present spectrum assignment is fixed and underutilized. Cognitive radio (CR) provides good solution to spectrum scarcity problem to accommodate new wireless applications. The network selection is an important mechanism in cognitive radio heterogeneous network (CRHN) to provide optimal Quality of Service (QoS) to both Primary Users (PUs) and Secondary Users (SUs). The aim of this work is to provide optimal QoS to SUs by appropriate network selection and channel assignment. The proposed FLACSA selects the best network while maximizing the data rate and minimizing the interference and cost simultaneously. Simulation results show the attractive performance of our proposed algorithm.