Plant diseases can have profound effects on the economy, impacting both local and global scales. These diseases can lead to substantial losses in agricultural productivity, affecting crop yields and quality. In this context, deep learning algorithms are widely acknowledged as effective solutions. However, the use of these black-box approaches raises concerns about trust in interpreting and validating the decisions generated by the models. This study proposes an explainable artificial intelligence (XAI) based plant disease classification system to classify and identify distinct ailments with improved accuracy. The system correctly identifies 38 different plant diseases with accuracy, precision, and recall as 99.69%, 98.27%, and 98.26%, respectively. These predictions are subjected to additional analysis employing the local interpretable model-agnostic explanations (LIME) framework to produce visual explanations aligning with prior beliefs and adhering to established best practices in explanations. This system will serve as a promising avenue for revolutionizing disease detection, fostering informed decision-making, and ultimately contributing to global food security.

The software project scheduling (SPS) problem deals with optimal allocation of employees to tasks such that the project is completed within budget and schedule. This is well addressed in the absence of dynamic events and has been demonstrated in SPS research with the static scenarios only. The software development methodologies have evolved where customer requirements are not initially locked but are flexible to take into account ongoing additional requirements or changes. In this regard, ‘new employee addition’ has emerged as one of the critical dynamic events that could significantly disrupt the project budget and schedule. In this paper, the SPS is modelled as multi-objective optimization problem and a novel approach is presented to deal with ‘new employee addition’ dynamic event; that uses domain knowledge to generate a robust schedule. The proposed heuristic is further evaluated on 18 dynamic benchmark scenarios and 3 real-world instances. The results show that the proposed heuristic besides its capability to handle dynamic events also demonstrates good convergence while maintaining good distribution of solutions in a dynamic environment. The principal target audience for this research are project managers in software development organizations who will get optimal project reschedule under ‘new employee addition’ dynamic event particularly in complex, large scale and multi-project settings.

In this paper, we propose a novel method for producing image captions through the utilization of Generative Adversarial Networks (GANs) and Vision Transformers (ViTs) using our proposed Image Captioning Utilizing Transformer and GAN (ICTGAN) model. Here we use the efficient representation learning of the ViTs to improve the realistic image production of the GAN. Using textual features from the LSTM-based language model, our proposed model combines salient information extracted from images using ViTs. This merging of features is made possible using a self-attention mechanism, which enables the model to efficiently take in and process data from both textual and visual sources using the self-attention properties of the self-attention mechanism. We perform various tests on the MS COCO dataset as well as the Flickr30k dataset, which are popular benchmark datasets for image-captioning tasks, to verify the effectiveness of our proposed model. The outcomes represent that, on this dataset, our algorithm outperforms other approaches in terms of relevance, diversity, and caption quality. With this, our model is robust to changes in the content and style of the images, demonstrating its excellent generalization skills. We also explain the benefits of our method, which include better visual–textual alignment, better caption coherence, and better handling of complicated scenarios. All things considered, our work represents a significant step forward in the field of picture caption creation, offering a complete solution that leverages the complementary advantages of GANs and ViT-based self-attention models. This work pushes the limits of what is currently possible in image caption generation, creating a new standard in the industry.