Glaucoma causes irreversible blindness. In 2020, about 80 million people worldwide had glaucoma. Existing machine learning (ML) models are limited to glaucoma prediction, where clinicians, patients, and medical experts are unaware of how data analysis and decision-making are handled. Explainable artificial intelligence (XAI) and interpretable ML (IML) create opportunities to increase user confidence in the decision-making process. This article proposes XAI and IML models for analyzing glaucoma predictions/results. XAI primarily uses adaptive neuro-fuzzy inference system (ANFIS) and pixel density analysis (PDA) to provide trustworthy explanations for glaucoma predictions from infected and healthy images. IML uses sub-modular pick local interpretable model-agonistic explanation (SP-LIME) to explain results coherently. SP-LIME interprets spike neural network (SNN) results. Using two different publicly available datasets, namely fundus images, i.e., coherence tomography images of the eyes
Process portfolio selection is an initial step organization take toward becoming process oriented. Usually, selecting a process as a subject of improvement is a multi-criteria problem. While there is a planetary of methods for process portfolio selection, in the current study, an extended hybrid fuzzy multi-criteria model is proposed. The main novelty of the proposed method is consideration of the effects of processes on sustainability and the strength of relationship among them. Paying attention to the principles of sustainable development and considering social and environmental criteria along with economic criteria will lead to the selection of a sustainable process portfolio. Therefore, criteria for selecting the process portfolio based on three pillars of sustainability, i.e., economic, social and environmental, were identified. Since the selection process can be considered as a complex and uncertain problem, after identification of process evaluation criteria, the magnitude of in