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Item Open Access Real-time Sound Anomaly Detection in Industrial Environments with Deep Learning(Faculty of Engineering and Natural Science, 2024) Zhailau M.This research uses deep learning to explore the field of sound anomaly detection in industrial settings in response to the growing need for improved industrial efficiency and safety. Centered on taking care of the constraints of conventional techniques, the study examines the effectiveness of several deep learning architectures, such as hybrid models, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), in identifying abnormal noises. With a focus on rigorous evaluation of datasets, preprocessing methods, and benchmarks, the survey offers a thorough picture of the most recent models and their uses in a variety of industrial areas. This research compares deep learning with traditional methods for sound anomaly identification and looks at performance evaluation criteria. Case studies and realworld implementations demonstrate the usefulness of the enhancements. While highlighting the need for innovative approaches to enhance the practical usefulness and robustness of deep learning-based sound anomaly detection in industrial settings, the research also points out its shortcomings and makes recommendations for future directions. This research not only contributes valuable insights into the intersection of deep learning and industrial sound analysis but also serves as a pivotal guide for researchers and practitioners seeking to navigate the complexities of deploying effective sound anomaly detection systems.Item Open Access Research on a UAV Distance Prediction System Based on Acoustic Data and Deep Learning(SDU University, 2025) Yembergenova A.Unmanned aerial vehicles (UAVs), also referred to as drones, have become increasingly popular in recent years, posing serious security and privacy issues. Concerns have been raised by their growing presence in public areas and civilian life as a result of incidents involving disturbances, privacy invasion, and unauthorised surveillance. This study aims to address these issues by creating an intelligent, sound-based system that can identify drones and determine how close they are to people or sensitive areas. The primary objective of this study was to assess the viability of classifying drone distance based on sound emissions using deep learning models and audio signals. Three zones—Zone 1, Zone 2, and Zone 3—each denoting varying degrees of proximity—were created from the drone sounds. Convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and a hybrid CNN-BiLSTM model were among the deep learning models examined in the study. With an average classification accuracy of 90%, the hybrid CNN-BiLSTM model outperformed the others. This model is very accurate at predicting drone distance zones because it successfully captured both spatial and temporal features from the audio recordings. These results imply that drone detection systems can be greatly improved by combining deep learning with audio-based classification. Such systems could significantly increase responsiveness and accuracy in detecting unauthorised UAV activity when paired with other sensory inputs in bimodal or multimodal frameworks. All things considered, this research advances acoustic sensing technologies to protect critical infrastructure and public safety from the increasing threat of rogue drone usage