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Flipped learning is a blended learning method based on academic engagement of students online (outside class) and offline (inside class). In this learning pedagogy, students receive lesson any time from lecture videos pre-loaded on digital platform at their convenience places and it is followed by in-classroom activities such as doubt clearing, problem solving

Collaborating filtering (CF) has become one of the most powerful approaches in the recommender system. Neighborhood-based CF uses a similarity measure to identity neighbors of an active user, and these neighbors play an essential role in the personalized recommendation. Recently introduced new heuristic similarity measure (NHSM) based CF is found to be performing well compared to the CF approaches, which use traditional measures like Pearson correlation coefficient (PCC), proximity impact popularity (PIP), etc. However, NHSM is not appropriately normalized, and it may mislead in finding neighbors in specific scenarios. In this paper, we propose an improved NHSM similarity measure to excel in the recommendation by overcoming the shortfall of NHSM. We propose to utilize hyperbolic trigonometric function for the normalization of each component of NHSM. Relative difference (RD) is exploited to address the misleading problem of NSHM. Experimental results demonstrate that our improved NHSM (i-NHSM) based CF outperforms NHSM based CF.

Sentiment analysis is one of the significant tasks in processing natural language by a machine. However, it is difficult for a machine to understand the feelings of a person and opinion about a topic. Many approaches have been introduced for analyzing sentiment from long text in recent past. In contrast, these approaches fail to address the small length text problem like Twitter data efficiently. Recent advances in the pre-trained contextualized embeddings like Bidirectional Encoder Representations from Transformers (BERT) show far greater accuracy than traditional embeddings. In this paper, we develop a novel architecture to tune the BERT using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. A task-specific layer is incorporated along with the BERT in the proposed model. Our model extracts sentiment from short texts, especially Twitter data. The extensive experiments show the superiority of our model over state-of-the-art models in sentiment analysis task across several gold standard datasets.

Flipped Classroom is a mode of learning which is developed based on students’ academic engagement inside and outside the classroom. In this learning pedagogy, students take lessons from pre-loaded lecture videos before coming to the classroom for doubt clearing, discussion, problem solving, etc. However, it is very difficult to ensure that students really pay attention while watching lecture videos.In this paper, we adopt a feature selection technique called 1D local binary pattern (1D-LBP) to analyze captured brain signals of the students. The proposed feature selection technique is termed as 1D Multi-Point Local Ternary Pattern (MP-LTP), which extracts unique statistical features from EEG signals. Subsequently, standard classification techniques are exploited to analyze the attention level of students. Experimental results show that the proposed method outperforms state-of-the-art classification techniques using LBP.

Unsupervised learning techniques are popular in detecting outliers in various domains. Many parametric and non-parametric outlier detection approaches have been proposed over the last decades. The existing neighborhood-based non-parametric unsupervised approaches like LOF, symmetric neighborhood, LDOF are proven to be effective when outliers are in a region of variable density. However, these techniques wrongly treat an outlier point as inlier in certain scenarios (outlier located between a dense cluster and close to a sparse cluster). In this work, we address this problem by exploiting the information of k-nearest neighbors and reverse nearest neighbors efficiently. We conducted experiments with synthetic, and four real-world datasets, and our proposed technique outperforms popular symmetric neighborhood, LDOF, LOF techniques, and recently introduced RDOS.