ANALYSIS AND DEVELOPMENT OF A FEATURE SELECTION & DEEP LEARNING METHOD FOR DETECTING AND LOCATING PERSONAL THINGS USING AUDIO SIGNALS & IMAGE PROCESSION
Authors: Maria A. 
Published: June 2024

This paper presents a method aimed at addressing the common challenge of locating personal belongings in our day-to-day lives. The inability to quickly find essential items before leaving for work or study can lead to frustration and worry. In urgent situations, such as needing to access ATMs or important files, the need to locate personal belongings may be disregarded, causing additional stress. To alleviate this tension, we have developed a deep learning-based Supervised Classification algorithm that enables the easy retrieval of personal items. By exploring various ideas and strategies employed when searching for lost items, we propose a solution that incorporates an alarm system utilizing beep sounds and artificial intelligence. The proposed method streamlines the process of finding personal belongings, promoting a state of serenity and reducing anxiety. The implementation of the method utilizes Python, simplifying the complex calculations required for efficient item retrieval. To develop this system, we collected a comprehensive dataset comprising audio signals and images of commonly misplaced or lost personal items. Leveraging feature selection techniques, we extract relevant audio and visual features, enhancing the discriminative power of the model. These features are then used to train a deep learning model, employing supervised classification algorithms to establish patterns and correlations between input data and the corresponding locations of personal items. Our proposed approach integrates audio analysis and image processing to determine the precise location of misplaced items. When a user misplaces an item, they can activate the system, which emits a distinctive beep sound while capturing images of the surrounding area. By analyzing the audio signals and comparing them with stored patterns, the algorithm identifies potential locations where the item might be found. Simultaneously, the captured images are processed to detect visual cues or distinct features associated with the item, further aiding in the search process. Experimental results demonstrate the effectiveness of our method in accurately detecting and locating personal belongings. The system achieves high accuracy rates, significantly reducing the time and effort required to find misplaced items. Furthermore, the integration of artificial intelligence and deep learning enables the system to adapt and improve over time, accommodating various personal items and environments. The analysis and development of a feature selection and deep learning-based method incorporating audio signals and image processing provide a practical solution for efficiently locating personal belongings. By utilizing this method, users can experience a sense of ease and efficiency, eliminating the unnecessary stress associated with misplaced items. Future enhancements may involve expanding the system's compatibility with mobile devices or incorporating additional sensory inputs, further enhancing its utility and versatility