A well-regarded as well as powerful method named the ‘analytic hierarchy process’ (AHP) uses mathematics and psychology for making and analysing complex decisions. This article aims to present a brief review of the consistency measure of the judgments in AHP. Judgments should not be random or illogical. Several researchers have developed different consistency measures to identify the rationality of judgments. This article summarises the consistency measures which have been proposed so far in the literature. Moreover, this paper describes briefly the functional relationships established in the literature among the well-known consistency indices. At last, some thoughtful research directions that can be helpful in further research to develop and improve the performance of AHP are provided as well.

United nation sustainable development goal two (UNSDG-2) aims to achieve the eradication of hunger along with the assurance of food security for all by 2030. This cannot be achieved without combining all forms of sciences including mathematics and statistics with agriculture practice to make agriculture sustainable. Agriculture has been considered the backbone of the economic systems of developing and developed countries. However, while practicing the activities, various conflicting issues incorporate and make the situation challenging for the agriculture industry in the decision-making process. Multi-criteria decision-making (MCDM) techniques can provide the optimal solution and they have proved to be viable in various complex decision-making problems of the real world like agriculture-related decision-making problems. They can help to find a suitable alternative for a particular complex situation. The analytical hierarchy process (AHP); a globally accepted method; has been a winning arc of the MCDM techniques. This article specifically reviews the application of AHP in various agriculture-related problems, the models used, the data sources used, and the overall precision attained using the different performance criteria of the past few years.

Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mechanism, and optimized hyperparameters to classify brain tumors into different categories as glioma, meningioma, pituitary tumor, and no tumor. The approach leverages state-of-the-art preprocessing techniques, transfer learning, and Gradient-weighted Class Activation Mapping (Grad-CAM) visualization on a dataset of 7023 MRI images to enhance both performance and interpretability. The proposed model achieves 99% test accuracy and impressive precision and recall figures and outperforms traditional approaches like Support Vector Machines (SVM) with Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Principal Component Analysis (PCA) by a significant margin. Moreover, the model eliminates the need for manual labelling—a common challenge in this domain—by employing end-to-end learning, which allows the proposed model to derive meaningful features hence reducing human input. The integration of attention mechanisms further promote feature selection, in turn improving classification accuracy, while Grad-CAM visualizations show which regions of the image had the greatest impact on classification decisions, leading to increased transparency in clinical settings. Overall, the synergy of superior prediction, automatic feature extraction, and improved predictability confirms the model as an important application to neural networks approaches for brain tumor classification with valuable potential for enhancing medical imaging (MI) and clinical decision-making.