In the modern day, sarcasm post is a regular activity in social media. It is a way of speech in which people use phrases that are not intended to convey their positive or negative feelings. People’s ability to accurately identify sarcasm is declining due to the pervasive usage of this method of communication in headlines on the news and on social media. This study introduces a novel hybrid deep learning model named CBMPBiLSTM (Convolutional Block with MaxPooling and Bi-directional LSTM), designed specifically for sarcasm detection in textual headlines. The model deploys an embedding layer that transforms textual input into dense vector representations, capturing semantic relationships. This is followed by a one-dimensional convolutional layer, which identifies local n-gram features within text. A MaxPooling1D layer is applied to reduce the dimensionality of the feature maps, effectively preserving the most significant and informative features. Further, a Bi-directional LSTM layer enables contextual learning from both past and future word sequences. The network concludes with a dense output layer using sigmoid activation for binary classification. In comparison with other typical hybrid models the proposed model is particularly optimized for news headlines, enabling superior generalization and advancing state-of-the-art performance in sarcasm detection. Experiments conducted on a labeled news headlines dataset demonstrate superior performance, achieving 95.84% accuracy. Comparative results against state-of-the-art methods validate the robustness and effectiveness of the proposed model in identifying sarcastic content.

Electrooculography (EOG)-based human–computer interface (HCI) systems have a wide range of applications due to their usability in inferring user's intention through eye movements. Because of limited eye activities, these systems generate limited commands, such as looking up, down, left, and right. The challenge is to utilize EOG signals in strategy-based applications, such as game-playing robots. This article presents a novel asynchronous controlled single-channel EOG-based HCI system that uses eyeblink signals to control a robotic manipulator in solving Guarini's puzzle. A puzzle board and three control buttons are presented in a graphical user interface. The user navigates to a target through a single eyeblink and selects it through a double eyeblink. Two data analysis algorithms, specifically logistic regression model and eyeblink detection module, are used to detect eyeblinks. Two online experiments were conducted with ten healthy subjects. In the screen-based puzzle-solving experiment, we achieved an average accuracy of 95.19% with a short response time (RT) of 0.96 s and an information transfer rate (ITR) of 268.14 bits/min. In the robot-assisted experiment, the proposed system achieved an average accuracy of 92.7%, with an RT of 0.98 s and an ITR of 252.92 bits/min. In both experiments, during the decision (idle) state, the average false positive rate (FPR) is found to be 0.02 events/min. This system generates sufficient commands to control a robotic manipulator in solving the puzzle on the physical puzzle board. In comparison to existing EOG-based gaming systems, the proposed system achieves high accuracy, low RT, lower FPR, and high ITR with fewer electrodes.

E-learning proves its importance in the diverse educational levels over traditional education. An adaptive e-learning system needs to deduce the learner model for adding personalization to instructional websites. The learner model is the perception repository about the e-content user, which can be inferred implicitly by employing meaningful semantic analysis of the text. In this research, a novel methodology is proposed to conceptually deduce the semantic learner model for personalized e-learning recommendations. Firstly, Conceptual Learner Model (CLM) is developed based on the learner’s behavior and context-based text semantic representation by exploiting concepts from the ConceptNet knowledge base, with a significant association of patterns and rules. Then, Expanded Contextual Learner Model (ECLM) is developed by exploring the latent semantics in graphs to add concepts with the common-sense meanings that exceeded the named entities. The learner’s knowledge graph is defined based on contextually associated concepts. Semantic relations in ConceptNet are exploited to extend learner models. The Normalized Particle Swarm Optimization (NPSO) algorithm is used to learn the importance of the relation types between the concepts. Thus, CLM and ECLM each are represented as a vector of weighted concepts in which updating is obtained automatically. The proposed recommendation system incorporates dynamic learner models to predict an appropriate e-content with the highest ranking, matching the true needs of a particular learner. Our simulation results show that the performance of ECLM is better Mean Reciprocal Rank (MRR) value 0.780 than other existing methods.