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The main objective of this study was to ascertain the impact of job clarity on nurses’ job satisfaction in the public hospitals of Sindh province of Pakistan. The results presented a valid and reliable measurement model so that a structural model could be built upon it for testing research hypotheses. Results indicate that job clarity has an insignificant impact on job satisfaction among nurses in Pakistan. Moreover, the fairness perception does not moderate; rather, it is found to be a strong predictor of nurses’ job satisfaction. In other words, people have a lack of clarity about tasks, roles, and responsibilities, often end up affecting their outcomes. Therefore, it is recommended to strengthen the element of fairness in jobs to boost job satisfaction. HR policies and general policy makers in the organization have a greater role in this regards to ensure that the work and task are divided on fair grounds and so the rewards.

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Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model checkpointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research.