Evacuation systems are crucial for minimizing casualties and property losses during emergencies. Understanding evacuee behavior in overcrowded situations is essential for developing effective evacuation strategies. However, evacuating large crowds from buildings with multiple exits is a challenging problem, especially when the exits are asymmetrical and the crowds are dense. This paper proposes a novel two-dimensional cellular automaton model for multi-exit evacuation, which simplifies evacuee decision-making in an asymmetrical exit layout within dense crowds. The model introduces the dynamic counting area technique, which dynamically adjusts the size and shape of the counting area around each exit based on the evacuee density level. This technique plays a crucial role in preventing the creation of overlapping counting areas between exits, which often leads to overestimated average evacuation time, unit evacuation time, and travel distance. Comparative analysis with previous dynamic parameter models (DPM) reveals notable results: the model achieved an average evacuation time of 201.20 time steps, a unit evacuation time of 0.50 time steps, and a travel distance of 28204 steps. These findings demonstrate the effectiveness of the improved model in addressing evacuation imbalances caused by asymmetrical exit layouts or evacuee distributions. Moreover, the study highlights the pivotal role of evacuee density around exits in determining exit choices during densely crowded emergency situations. The improved model can be applied to various scenarios and settings where multi-exit evacuation is required, such as stadiums, airports, or shopping malls.

The prevalence of plant diseases presents a substantial challenge to global agriculture, significantly impacting both production levels and economic stability in numerous countries. This study focuses on the early detection of two prevalent diseases affecting barley leaves: net blotch and spot blotch. We introduce a novel model designed for the accurate detection and classification of these diseases. The model employs advanced pre-processing techniques, including the transformation of images into the CIELAB color space and the segmentation of affected areas, to enhance disease identification accuracy. Key shape properties characterizing the diseased regions are extracted and analyzed to distinguish between the two diseases. A critical component of our approach is the feature selection phase, aimed at identifying the most pertinent and informative features, thereby minimizing classification errors and maximizing model accuracy with a minimal set of shape properties. To optimize this process, we have incorporated the Grasshopper Optimization Algorithm, which effectively identifies the optimal shape properties for feature selection. The final classification is executed using a Back Propagation Neural Network Classifier. The efficacy of our model was tested using images of barley afflicted with the specified diseases. The results were compelling, with the model achieving a remarkable accuracy rate, largely attributable to the integration of the grasshopper optimization algorithm in the feature selection stage.