Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work.

Magnetic Resonance Imaging (MRI) classification and segmentation of brain tumors are still an important problem in medical imaging because tumors are typically heterogeneous and multicellular. Old-fashioned diagnostics do not scale, generalize or compute efficiently, and hence do not apply clinically. In order to overcome these problems, the research introduces a new Dynamic Attention-Augmented Neural Network (DAANN) that will improve the classification accuracy, robustness and computational efficiency of brain tumors. The proposed DAANN features a dynamic attention system that enables it to choose the diagnostically relevant areas from MRI images automatically, enabling it to perform even in noisy or sparse data. Combined with feature extraction over time, the model captures morphological and sequential dependencies to render full tumor analysis. When trained on benchmark MRI images, the DAANN had a classification precision of 94.8% and topped models like CNN and BiLSTM. The model was also robust to noise — with 89.0% accuracy under moderate noise levels — and operated efficiently using very little training data, with 78.0% accuracy on 10% of the training set. These findings point to the DAANN’s real-time clinical availability in resource-constrained settings. As it takes up the challenge of what is possible, this work adds value to medical imaging as it provides a scalable and reproducible way to diagnose brain tumors. The work in the future will be to extend the model to multi-organ classification and to make it more easily read by clinical decision makers.