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Sign language for communication is efficacious for humans, and vital research is in progress in computer vision systems. The earliest work in Indian Sign Language (ISL) recognition considers the recognition of significant differentiable hand signs and therefore often selecting a few signs from the ISL for recognition. This paper deals with robust modeling of static signs in the context of sign language recognition using deep learning-based convolutional neural networks (CNN). In this research, total 35,000 sign images of 100 static signs are collected from different users. The efficiency of the proposed system is evaluated on approximately 50 CNN models. The results are also evaluated on the basis of different optimizers, and it has been observed that the proposed approach has achieved the highest training accuracy of 99.72% and 99.90% on colored and grayscale images, respectively. The performance of the proposed system has also been evaluated on the basis of precision, recall and F -score. The system also demonstrates its effectiveness over the earlier works in which only a few hand signs are considered for recognition.

The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. Misleading or unreliable information in the form of videos, posts, articles, URLs is extensively disseminated through popular social media platforms such as Facebook and Twitter. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. Motivated by the need for automated detection of fake news, the goal is to find out which classification model identifies phony features accurately using three feature extraction techniques, Term Frequency–Inverse Document Frequency (TF–IDF), Count-Vectorizer (CV) and Hashing-Vectorizer (HV). Also, in this paper, a novel multi-level voting ensemble model is proposed. The proposed system has been tested on three datasets using twelve classifiers. These ML classifiers are combined based on their false prediction ratio. It has been observed that the Passive Aggressive, Logistic Regression and Linear Support Vector Classifier (LinearSVC) individually perform best using TF-IDF, CV and HV feature extraction approaches, respectively, based on their performance metrics, whereas the proposed model outperforms the Passive Aggressive model by 0.8%, Logistic Regression model by 1.3%, LinearSVC model by 0.4% using TF-IDF, CV and HV, respectively. The proposed system can also be used to predict the fake content (textual form) from online social media websites.

Word sense disambiguation (WSD) identifies the right meaning of the word in the given context. It is an indispensable and critical application for all the natural language processing tasks. In this paper, two deep learning techniques multilayer perceptron and long short-term memory (LSTM) have been individually inspected on the word vectors of 66 ambiguous Punjabi nouns for an explicit WSD system of Punjabi language. The inputs to the deep learning techniques are the simple word vectors derived directly from manually sense-tagged corpus of Punjabi language. The multilayer perceptron has outperformed the LSTM deep learning technique for WSD task of Punjabi language. Six traditional supervised machine learning techniques have also been tested on same dataset using unigram and bigram feature sets. A comparison between deep learning techniques and traditional six supervised machine learning techniques clearly indicates that the deep learning techniques using simple word vectors outperforms the earlier techniques.

Over the last few years, there has been a rapid growth in digital data. Images with quotes are spreading virally through online social media platforms. Misquotes found online often spread like a forest fire through social media, which highlights the lack of responsibility of the web users when circulating poorly cited quotes. Thus, it is important to authenticate the content contained in the images being circulated online. So, there is a need to retrieve the information within such textual images to verify quotes before its usage in order to differentiate a fake or misquote from an authentic one. Optical Character Recognition (OCR) is used in this paper, for converting textual images into readable text format, but none of the OCR tools are perfect in extracting information from the images accurately. In this paper, a method of post-processing on the retrieved text to improve the accuracy of the detected text from images has been proposed. Google Cloud Vision has been used for recognizing text from images. It has also been observed that using post-processing on the extracted text improved the accuracy of text recognition by 3.5% approximately. A web-based text similarity approach (URLs and domain name) has been used to examine the authenticity of the content of the quoted images. Approximately, 96.26% accuracy has been achieved in classifying quoted images as verified or misquoted. Also, a ground truth dataset of authentic site names has been created. In this research, images with quotes by famous celebrities and global leaders have been used. A comparative analysis has been performed to show the effectiveness of our proposed algorithm.

The dramatic impact of the COVID-19 pandemic has resulted in the closure of physical classrooms and teaching methods being shifted to the online medium.To make the online learning environment more interactive, just like traditional offline classrooms, it is essential to ensure the proper engagement of students during online learning sessions.This paper proposes a deep learning-based approach using facial emotions to detect the real-time engagement of online learners. This is done by analysing the students’ facial expressions to classify their emotions throughout the online learning session. The facial emotion recognition information is used to calculate the engagement index (EI) to predict two engagement states “Engaged” and “Disengaged”. Different deep learning models such as Inception-V3, VGG19 and ResNet-50 are evaluated and compared to get the best predictive classification model for real-time engagement detection. Varied benchmarked datasets such as FER-2013, CK+ and RAF-DB are used to gauge the overall performance and accuracy of the proposed system. Experimental results showed that the proposed system achieves an accuracy of 89.11%, 90.14% and 92.32% for Inception-V3, VGG19 and ResNet-50, respectively, on benchmarked datasets and our own created dataset. ResNet-50 outperforms all others with an accuracy of 92.3% for facial emotions classification in real-time learning scenarios.

Evaluating genetically superior genotypes is essential for developing new hybrid varieties. This study aimed to assess the genetic diversity of 28 edible-podded pea genotypes by analyzing phenological traits, vigor, yield, and biochemical traits across two distinct agro-climatic zones in India. Significant variation was observed for most traits, with high genotypic and phenotypic coefficients of variation, heritability, and genetic advance, especially in vigor, yield, and biochemical traits. Phenological traits, except for the node at which the first flower appeared, exhibited minimal variability, indicating a high degree of uniformity. Yield per plant was negatively correlated with plant height but positively correlated with pod length, the number of seeds per pod, the number of pods per plant, and pod weight, indicating the potential for the simultaneous selection of these traits in breeding programs. Principal component analysis (PCA) identified six components explaining over 75% of the total variation, with yield and biochemical traits contributing the most to the observed diversity. These findings provide crucial insights for breeders aiming to improve quantitative traits, supporting the development of high-yielding and climate-resilient edible-podded pea varieties in India.

This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting (“wisdom of the crowd”) and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents’ expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents’ classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods.

This study assessed eleven elements in 24 edible-podded peas, including sugar snap pea and snow pea genotypes aiming to identify promising parents for nutraceutical breeding. Elemental concentrations of pods (dry weight basis) were estimated through inductively coupled plasma-optical emission spectroscopy (ICP-OES). The ranges for these elements varied significantly, highlighting the diverse elemental profiles within the edible-podded pea genotypes. All the elements exhibited a high genotypic and phenotypic coefficient of variation along with considerable heritability and hereditary progress. Positive and significant correlations were recorded among all elements, suggesting the potential for simultaneous selection for these traits. Principal component analysis (PCA) revealed that the first two components accounted for 80.56% of the variation. Further, cluster analysis, based on Euclidean distance, grouped the 24 cultivars into two major clusters. Cluster I exhibited higher means for all estimated concentrations compared to Cluster II. Notably, Dwarf Grey Sugar and Arka Sampoorna from the snap pea group and PED-21-5 and Sugar Snappy from the sugar snap pea in Cluster II demonstrated superior elemental concentration in whole pods. The selected edible-podded pea genotypes serve as valuable genetic resources for new cultivar development, particularly in biofortification efforts targeting whole pod nutrient composition.

In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis. This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically. The current status of Indian languages in sentiment analysis is classified according to the Indian language families. The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described. Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented. This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions. In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools. Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.

Due to the COVID-19 crisis, the education sector has been shifted to a virtual environment. Monitoring the engagement level and providing regular feedback during e-classes is one of the major concerns, as this facility lacks in the e-learning environment due to no physical observation of the teacher. According to present study, an engagement detection system to ensure that the students get immediate feedback during e-Learning. Our proposed engagement system analyses the student’s behaviour throughout the e-Learning session. The proposed novel approach evaluates three modalities based on the student’s behaviour, such as facial expression, eye blink count, and head movement, from the live video streams to predict student engagement in e-learning. The proposed system is implemented based on deep-learning approaches such as VGG-19 and ResNet-50 for facial emotion recognition and the facial landmark approach for eye-blinking and head movement detection. The results from different modalities (for which the algorithms are proposed) are combined to determine the EI (engagement index). Based on EI value, an engaged or disengaged state is predicted. The present study suggests that the proposed facial cues-based multimodal system accurately determines student engagement in real time. The experimental research achieved an accuracy of 92.58% and showed that the proposed engagement detection approach significantly outperforms the existing approaches.