Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder—Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network’s depth.

The evolving field of deep learning has automated intelligent surveillance and criminal activity detection systems. This paper provides refined optimized deep learning architecture systems aimed at improving crime detection using facial recognition, real-time video surveillance systems, and weapon detection. These systems utilize the latest neural network structures, chiefly CNNs, RNNs, and hybrid deep learning models, to derive accuracy, real-time analysis, and scalability. The automated facial recognition systems track users' behaviors, registers recognized users, recognizes suspicious behaviors, and detects weapons at video feed frames. Using spatial-temporal analysis helps detect and reduce response delays in public areas, transport systems, and other sensitive places. Important issues such as dataset deficiencies, real-time computing limits, aggressive attacks, and ethical concerns on surveillance AI are given attention. Transfer learning, model compression, edge computing, and AI transparency are some solutions put forward. The paper addresses these challenges and emphasizes the need for diverse, representative training datasets to strengthen system dependability and equity. Future research and development may benefit from the discussion highlighting transformer-based architectures, multimodal fusion, and other emerging trends which incorporate reinforcement learning. This review furthers the state of the art by integrating advanced technology development and documenting noted advancements, turning them into actionable frameworks by formulating real-world deployment strategies. The primary contribution of this paper is to assist researchers, developers, and even policymakers comprehend in-depth, the potential deep learning methods offer in the design of surveillance systems for criminal activity detection that are safe, smart, and ethically responsible.