Causal traces show that the last token of the subject name is not always decisive.

Traditional fish farming practices often lead to inefficient feeding, resulting in environmental issues and reduced productivity. We developed an innovative system combining computer vision and IoT technologies for precise Tilapia feeding. Our solution uses real-time IoT sensors to monitor water quality parameters and computer vision algorithms to analyze fish size and count, determining optimal feed amounts. A mobile app enables remote monitoring and control. We utilized YOLOv8 for keypoint detection to measure Tilapia weight from length, achieving \textbf{94\%} precision on 3,500 annotated images. Pixel-based measurements were converted to centimeters using depth estimation for accurate feeding calculations. Our method, with data collection mirroring inference conditions, significantly improved results. Preliminary estimates suggest this approach could increase production up to 58 times compared to traditional farms. Our models, code, and dataset are open-source~\footnote{The code, dataset, and models are available upon reasonable request.

Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijacking ineffective. Such a technique is made possible by integrating the power of artificial intelligence and Machine Learning (ML). Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats. In this paper, we propose a novel Federated Learning (FL) approach that protects the anonymity of user data and maintains the security of his data. We present a warmup approach that provides a significant accuracy increase. In addition, we leverage the transfer learning technique based on feature extraction to boost the models' performance. Our extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and UMDAA-02-FD, show a significant increase in user authentication accuracy while maintaining user privacy and data security.

Health challenges represent one of the long-standing issues in the Arab region that hinder its ability to develop. Prevalence of diseases such as cardiovascular diseases, liver cirrhosis and cancer among many others has contributed to the deteriorated health status across the region leading to lower life expectancy compared to other regions. For instance, the average life expectancy in the Arab world is approximately 70 years, which is at least 10 years lower than most high-income countries.2 Among many directions of healthcare development across the region, biomedical computing research represents one main arm of tackling health challenges. Advances in computational technologies have enabled the emergence of biomedical computing as one of the most influential research areas worldwide.

A man holds a traditional lamp from the balcony of his house as torches and candles illuminate houses and high rise residential buildings as Indians mark the country's fight against the coronavirus pandemic a suburb of New Delhi, India