Attention-Deficit Hyperactivity Disorder (ADHD) represents a challenging neurodevelopmental disorder that consistently displays three major symptoms involving inattention and hyperactivity alongside impulsivity. Traditional approaches for diagnosis use behavioral evaluations that create both wrong conclusions and delayed help timing. This research develops a complete diagnostic solution involving deep learning federated learning and blockchain security to analyze actigraphy signals originating from IoMT devices. This method first uses UMAP as well as PCA and t-SNE to reduce data dimensions before implementing a hybrid CNN-Transformer neural network to achieve improved classification results. A distributed learning method helps medical institutions run model training autonomously while satisfying privacy rules and addressing data centralization challenges. Model updates on blockchain systems gain protection through smart contracts and cryptographic hashing to stop adversarial attacks and sustain data authenticity. Laboratory tests reveal that this approach reaches 99.2% classification precision without significant performance impact, establishing its effectiveness. This presented study provides on-the-next level ADHD diagnosis features with the help of an AIbased system that ensures privacy and guarantees tampering and scalable operations. Such results allow advancing accurate medical works by real-time monitoring of ADHD and offer safe application of medical Artificial Intelligence to distributed healthcare processes. This will provide objective and credible evaluations that will exist on a global scale.

The deployment of sensor nodes in underwater environment is constrained by some resources of sensor node like: energy, processing speed, cost and memory and also affected by dynamic nature of water. The main purpose of node deployment is to get the sensed data from the underwater environment. One of the major tasks is to cover the whole area in underwater and also there must be a full connectivity in the network so that each sensor nodes are able to send their data to the other sensor node. Some researchers use the concept of node mobility for better coverage and connectivity. This work proposes an efficient node deployment technique for enhancing the coverage and connectivity in underwater sensor network. Simulation results show good performance in terms of area coverage and connectivity.