A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture. Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets. Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency. Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters

Leveraging a Hybrid Deep Learning Architecture for Efficient Emotion Recognition in Audio Processing

This paper presents a novel hybrid deep learning architecture for emotion recognition from speech signals, which has garnered significant interest in recent years due to its potential applications in various fields such as healthcare, psychology, and entertainment. The proposed architecture combines modified ResNet-34 and RoBERTa models to extract meaningful features from speech signals and classify them into different emotion categories. The model is evaluated on five standard emotion recognition datasets, including RAVDESS, EmoDB, SAVEE, CREMA-D, and TESS, and achieves state-of-the-art performance on all datasets. The experimental results show that the proposed hybrid architecture outperforms existing emotion recognition models, achieving high accuracy and F1 scores for emotion classification. The proposed architecture is promising for real-time emotion recognition applications and can be applied in various domains such as speech-based emotion recognition systems, human-computer interaction, and virtual assistants

Properties of Bacillus anthracis spores prepared under various environmental conditions

Bacillus anthracis makes highly stable, heat-resistant spores which remain viable for decades. Effect of various stress conditions on sporulation in B. anthracis was studied in nutrient-deprived and sporulation medium adjusted to various pH and temperatures. The results revealed that sporulation efficiency was dependent on conditions prevailing during sporulation. Sporulation occurred earlier in culture sporulating at alkaline pH or in PBS than control. Spores formed in PBS were highly sensitive towards spore denaturants whereas, those formed at 45° C were highly resistant. The decimal reduction time (D-10 time) of the spores formed at 45° C by wet heat, 2 M HCl, 2 M NaOH and 2 M H<SUB>2</SUB>O<SUB>2</SUB> was higher than the respective D-10 time for the spores formed in PBS. The dipicolinic acid (DPA) content and germination efficiency was highest in spores formed at 45° C. Since DPA is related to spore sensitivity towards heat and chemicals, the increased DPA content of spores prepared at 45° C may be responsible for increased resistance to wet heat and other denaturants. The size of spores formed at 45° C was smallest amongst all. The study reveals that temperature, pH and nutrient availability during sporulation affect properties of B. anthracis spores