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The emergence of generative adversarial networks (GANs) has ignited substantial interest in the domain of synthesizing images from textual descriptions. This approach has demonstrated remarkable versatility and user-friendliness in producing conditioned images, showcasing notable progress in areas like diversity, visual realism, and semantic alignment in recent years. Notwithstanding these developments, the discipline still faces difficulties, such as producing high-resolution pictures with several objects and developing trustworthy evaluation standards that are in line with human vision. The goal of this study is to provide a comprehensive overview of the state of stochastic text-to-image creation models as of right now. It examines how they have changed over the previous five years and suggests a classification system depending on the degree of supervision required. The paper highlights shortcomings, provides a critical evaluation of current approaches for assessing text-to-image synthesizing models, and suggests further study areas. These goals include improving the training of models and designs for architecture, developing more reliable assessment criteria, and fine-tuning datasets. This review, which focuses on text-to-image synthesizing, is a useful addition to earlier surveys on adversarial networks that are generative and offers guidance for future studies on the subject.

The generation of high-quality images from textual descriptions is a challenging task in computer vision and natural language processing. The goal of text-to-image synthesis, a current topic of research, is to produce excellent images from written descriptions. This study proposes a hybrid approach to evaluating a dataset consisting of various text-image pairs by efficiently combining conditional generative adversarial networks (C-GAN), attention mechanisms, and contrastive learning (C-GAN+ATT+CL). We suggest a two-step method to improve image quality that starts by utilizing generative adversarial networks (GANs) with attention mechanisms to create low-resolution images and then contrastive learning to improve. Contrastive learning modules train on a separate dataset of high-resolution pictures; GANs learn on datasets of low-resolution text and image pairs. The Conditional GAN with Attention Mechanism and Contrastive Learning Method provides state-of-the-art performance in terms of image quality, diversity, and visual realism, among the several methods. The results of this study demonstrate that the proposed approach works better than all other methods, achieving an Inception Score (IS) of 35.23, a Fréchet Inception Distance (FID) of 18.2, and an R-Precision of 89.14. Our findings demonstrate that our “C-GAN+ATT+CL” approach significantly improves image quality and diversity and offers exciting paths for further study.

Abstract Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting “Not Backorder” and 75% for predicting “backorder,” outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.

The definition of human-computer interaction (HCI) has changed in the current year because people are interested in their various ergonomic devices ways. Many researchers have been working to develop a hand gesture recognition system with a kinetic sensor-based dataset, but their performance accuracy is not satisfactory. In our work, we proposed a multistage spatial attention-based neural network for hand gesture recognition to overcome the challenges. We included three stages in the proposed model where each stage is inherited the CNN; where we first apply a feature extractor and a spatial attention module by using self-attention from the original dataset and then multiply the feature vector with the attention map to highlight effective features of the dataset. Then, we explored features concatenated with the original dataset for obtaining modality feature embedding. In the same way, we generated a feature vector and attention map in the second stage with the feature extraction architecture and self-attention technique. After multiplying the attention map and features, we produced the final feature, which feeds into the third stage, a classification module to predict the label of the correspondent hand gesture. Our model achieved 99.67%, 99.75%, and 99.46% accuracy for the senz3D, Kinematic, and NTU datasets.