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Text Augmentation-Based Model for Emotion Recognition Using Transformers

Computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： fida mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Determination and inheritance of phytic acid as marker in diverse genetic group of bread wheat

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American Journal of Molecular Biology 2013年第3期3卷 158-164页

作者： Ijaz Ahmad fida mohammad Aurang Zeb Ijaz Rasool Noorka Farhatullah Sultan Akber Jadoon Department of Plant Breeding and Genetics The University of Agriculture Peshawar Pakistan Nuclear Institute for Food and Agriculture Tarnab Pakistan University College of Agriculture University of Sargodha Sargodha Pakistan University of Swabi Swabi Pakistan

Phytic acid (Myo-inositol 1,2,3,4,5,6 hexa-kisphophate) is a storage form of phosphorus and can accumulate to the levels as high as 35% in the wheat kernel. Phytic acid acts as an inhibitor for macronutrients as well as micronutrients and located in the bran of wheat kernel. Due to its inhibitory role, a high concentration of phytic acid is undesirable as it hinders the bio-availability of some essential nutrients such as Fe, Mg, Ca, Zn and Cu, etc. In order to check the inheritance of phytic acid in wheat kernels, phytic acid concentration was initially determined in kernels of 10 wheat genotypes to identify two contrasting genetic groups for diallel analysis. Based on pre-screening results of 10 wheat genotypes, five wheat genotypes (3 with high and 2 with low phytic acid concentration) were crossed in all possible combinations during 2007-2008 by 5 × 5 full diallel mating fashion to insight the inheritance of phytic acid and other yield contributing traits. All 20 F1 hybrids and five parental genotypes revealed significant differences statistically, except plant maturity. The narrow and broad sense heritability estimates varied widely among traits for spike length (0.17, 0.62), spikelets spike-1 (0.35, 0.74), tillers plant-1 (0.05, 0.52) and phytic acid concentration (0.01, 0.86). The values for phytic acid concentration ranged from 0.56% to 3.43% among F1 hybrids and 1.06 to 3.67% for parental genotypes. F1 hybrids, Ps-2005 × Ghaznavi (0.56%), AUP-4006 × Ps-2004 (0.74%), Janbaz × Ps-2004 (0.89%) and Janbaz × Ps-2005 (1.01%), had the lowest concentration of phytic acid. The study concluded that F1 hybrids with low phytic acid concentration could yield desirable segregants.

关键词： Phytic Acid Diallel Analysis Inheritance Heritability Yield Traits

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