Design and Realization of Logic Gates or Functions Using Vertical TEFT Structures

Silicon - The present paper aims to propose silicon-based double-gate vertical TFET (DGV-TFET) for implementing different Boolean functions involving basic gates and universal gates. The motivation...     

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification. The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data. Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification. Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization. Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique. Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm. To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.     

Exploration on Solvatochromic Sensing Probe of Phenol Blue for L-Lactic Acid Detection: Effective Strategy to Obtain Reliable Capacitive Sensor

This study systematically investigates a capacitive sensor applied with phenol blue (PhB)-based sensing medium for detection of L-lactic acid (LA), as a health monitoring indicator. PhB is a substance with solvatochromic effect, inducing the change in capacitance by exposure to polar molecules. However, the capacitive LA sensor with a flat-structured PhB/polyvinylchloride (PVC) composite-sensing medium is observed to have a problem in that sensing capacitance variation saturate quickly with increasing the LA solution concentration. This main cause can be analyzed that the interaction of proton from LA molecule with the lone pair electrons of the PhB molecule acts as a major factor on the sensing characteristics rather than the solvatochromic behavior of PhB molecule. Therefore, a strategy is adopted to introduce a porous structure to the PhB/PVC composite-sensing medium to maximize the interaction of PhB with protons, which is implemented through solvent and non-solvent exchange methods. Consequently, the sensitivity and linearity of the porous-structured LA sensor are 2.99 pF mm ⁻¹ and 0.966 over LA concentrations ranging from 0 to 100 mm , respectively, which is a significant improvement over that of the flat-structured one. Notably, the sensing performance remained unchanged even after a month of storage under normal ambient conditions.