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Go to Editorial ManagerIdentifying fish species in natural aquatic environments remains challenging due to changing light conditions, turbid water, and complex underwater scenes. Most current deep-learning models rely on controlled datasets, which limits their use in real-world settings. This study presents Auto Fish, a mobile deep-learning system for real-time, offline fish species identification on Android devices. The system uses the MobileNetV2 architecture, optimized with TensorFlow Lite for processing on the device. This approach ensures high accuracy while keeping computational costs low. We trained and evaluated the model on a balanced dataset of 8,000 annotated images, including nine marine species: Sea bass, Red sea bream, Horse mackerel, Gilt-head bream, Shrimp, Black sea sprat, Trout, Red mullet, and Striped red mullet. Extensive preprocessing, image enhancement, and stratified sampling helped the model perform well despite variations in lighting and background conditions. The experimental results showed a validation accuracy of 99.2%, with both macro and micro Precision, Recall, and F1-scores around 99.3%, and an average False Positive Rate (FPR) of 0.09%. The system supports offline recognition, cloud syncing via Firebase, and delivers real-time results within 4.2 seconds per image on mid-range smartphones. These findings show that Auto Fish can effectively classify fish species in the field while remaining efficient and easy to use. This work offers a practical AI-based solution that connects research with ecological monitoring, empowering citizen scientists and conservationists to document biodiversity using mobile technology.
In the past few years, all over the world, crime against children has been on the rise, and parents always worry about their children whenever they are outside. For this reason, tracking and monitoring children have become a considerable necessity. This paper presents an outdoor IoT tracking system which consists of a child module and a parent module. The child module monitors the child location in real time and sends the information to a database in the cloud which forwards it to the parent module (represented as a mobile application). This information is shown in the application as a location on Google maps. The mobile application is designed for this purpose in addition to a number of extra functions. A Raspberry Pi Zero Wireless is used with a GSM/GPS module on shield to provide mobile communication, internet and to determine location. Implementation results for the suggested system are provided which shows that when the child leaves a pre-set safe area, a warring message pops up on the parent’s mobile and a path from the current parent location to the child location is shown on a map.