Cognitive workload is one of the widely invoked human factors in the areas of human–machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain–computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.

In this paper, we study optimal spectrum sensing schedule and optimal amount of transmission energy for harvested energy powered cognitive radio networks (CRN). We consider a scenario where cognitive users (CUs) exchange secrete messages in the presence of multiple movable eavesdroppers. The location set of eavesdroppers is modeled by Homogenous Poison Point Process (HPPP). To satisfy the instantaneous interference constraint with primary network, CU transmitters have adaptive power antennas. At the beginning of each time slot, CU transmitter decides to become active to sense channel or remain inactive to transmit if channel is vacant or not, on the basis of remaining energy in battery and expected secrecy transmission rate (ESTR). The purpose of this manuscript is to get future base reward for secrecy transmission rate of CRNs.