In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.

Environmental conditions of ocean are crucial for predicting harmful events, which gives an impact on marine ecosystems and human well-being. Various threats, such as ocean acidification, harmful algal blooms, and coral bleaching, need early monitoring of variables, including temperature, acidity, pollution levels, and biodiversity, to mitigate the adverse effects. This proactive approach enables conservation efforts that safeguard marine life, fisheries, and coastal communities while promoting sustainable ocean stewardship. Deep learning techniques play a pivotal role in this endeavour by processing underwater acoustic recordings. It gives accurate results to identify marine species, detect changes in their behavior, and predict environmental conditions based on sound patterns. The proposed work suggests that, combination of Long Short Term Memory(LSTM) and Graphical Neural Network(GNN) which is used to predict the harmful changes over the ocean. LSTM and GNN architectures establish an indirect relationship, shedding light on emerging trends and potential threats.

The marine benthic zone is host to a vast array of species that are vital to both ecosystem function and biodiversity conservation. Understanding these underwater communities is essential for ecological research, conservation efforts, and sustainable resource management. However, traditional surveying techniques face significant challenges in the underwater environment. This research extends strategies for underwater biological monitoring by offering a reliable and useful method for detecting benthos in marine benthic zones. Ultimately, by promoting conservation initiatives and sustainable management techniques in marine settings, the proposed Single Shot Detector (SSD) based system may speed up underwater surveys, make data collection easier, and further our knowledge of benthic ecosystems.