Healthcare IoT seeks to use technology to better patient care, optimize operational efficiency, and provide remote monitoring and management of health issues. Resource management is crucial in the context of Health Internet of Things (HIoT) since it enhances the performance of healthcare services. This research paper proposes a resource management model in healthcare Internet of Things (IoT) by using deep learning and bio-inspired algorithms. A deep learning model LSTM model is used to resource failure prediction and bio-inspired algorithms are used for resource allocation and load balancing. An accurate prediction of resource utilization and effective resource management algorithm will improve the overall performance of IoT services for Health care application. The proposed approach incorporates deep learning methods to identify and anticipate anomalies, enabling the proactive identification of future problems or resource failures and resource utilization. In addition, bio-inspired algorithms are used to dynamically distribute resources and optimize system performance in real-time. The efficacy of the proposed fault-tolerant method is proved by extensive simulations and performance tests. The experiment results show the improvement in performance parameters as compared to state-of-the-art resource management models

The financial sector is grappling with significant challenges in detecting cyber attacks, leading to potential short- and long-term financial losses for banks and other institutions. The statistical and machine learning methods have been effective in identifying suspicious activities, they have struggled to achieve a balance between recall and precision. To improve accuracy, this paper introduces a novel approach that employs deep learning and bio-inspired algorithms to detect suspicious activities. The proposed model analyzes transactional patterns, quantities, and temporal aspects using a carefully curated dataset of labeled transactions. The model shows promising results in distinguishing between legitimate and fraudulent operations, achieving a balance between recall and precision. Further, many in the industry are transitioning to cloud computing infrastructures to enhance application performance. However, these infrastructures are not ideal for delay-sensitive applications, such as those in the medical and finance sectors. To address communication delays, fog computing has emerged as a new paradigm. The  proposed model was simulated using Python and the Google Colab framework, and  experimental results shows that  improved accuracy and a balanced recall and precision.