Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Fusion of mel and gammatone frequency cepstral coefficients for speech emotion recognition using deep C-RNN

Abstract: Emotions play a significant role in human life. Recognition of human emotions has numerous tasks in recognizing the emotional features of speech signals. In this regard, Speech Emotion Recognition (SER) has multiple applications in various fields of education, health, forensics, defense, robotics, and scientific purposes. However, SER has the limitations of data labeling, misinterpretation of speech, annotation of audio, and time complexity. This work presents the evaluation of SER based on the features extracted from Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to study the emotions from different versions of audio signals. The sound signals are segmented by extracting and parametrizing each frequency calls using MFCC, GFCC, and combined features (M-GFCC) in the feature extraction stage. With the recent advances in Deep Learning techniques, this paper proposes a Deep Convolutional-Recurrent Neural Network (Deep C-RNN) approach to classify the effectiveness of learning emotion variations in the classification stage. We use a fusion of Mel–Gammatone filter in convolutional layers to first extract high-level spectral features then recurrent layers is adopted to learn the long-term temporal context from high-level features. Also, the proposed work differentiates the emotions from neutral speech with suitable binary tree diagrammatic illustrations. The methodology of the proposed work is applied on a large dataset covering Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. Finally, the proposed results which obtained accuracy more than 80% and have less loss are compared with the state of the art approaches, and an experimental result provides evidence that fusion results outperform in recognizing emotions from speech signals.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Abstract: Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Pseudocereals as super foods of 21st century: Recent technological interventions

Abstract: Cereals have been known to play a pivotal role to meet the demand of the human population since time immemorial. Cereals like corn, wheat and rice constitute approximately about 80% food consumption and are biofortified to improve the vitamin and other essential micro-nutrients. On the other hand, pseudocereals are naturally enriched with these essential micronutrients, but have not been explored for large-scale production and consumption till date. In this context, Food and Agriculture Organization (FAO) has identified many plants as under-utilized, which can significantly contribute for improving nutrition and health, enhance food basket and livelihoods, future food security and sustainable development. These underutilized crops offer an immense potential in the functional food sector to combat hidden hunger crisis and offer the options of income generation. Moreover, since underutilized crops are closely knit to cultural traditions and therefore are envisaged to have a role in supporting social diversity. To explore these neglected or lost crops, there is an increasing interest in research and development that needs heightened direction and focus. We need to develop a multidisciplinary approach that involves many stake holders to review and accelerate the domestication of these neglected crops. These reviews focus on nutritional profile of emerging pseudocereals and analyze the various constraints of their integration into the global food system.