Real-time pneumonia prediction using pipelined spark and high-performance computing

DPro-SM - A distributed framework for proactive straggler mitigation using LSTM

The recent advancement in deep learning with growth in big data and high-performance computing is Distributed Deep Learning. The rapid rise in the volume of data and network complexity has led to significant growth in DDL. Distribution of the network leads to high communication and computation among the nodes, which leads to high training time and lower accuracy. The primary reason for the delay in communication is the presence of straggler nodes which causes the bottleneck in communication. Due to the enormous volume of parameter transfer, Distributed Deep Learning's data parallelism incurs substantial communication costs. The newly developed model-parallel methods may minimize the communication effort; however, this results in load imbalance and severe straggler issues: the proposed model DPro-SM, a distributed framework for proactive straggler mitigation using LSTM in distributed deep learning. DPro-SM uses LSTM to predict the completion time of each worker and proactively allocates resources to reduce the overall training time. The results show that DPro-SM can significantly reduce the training time and improve the scalability and efficiency of large-scale machine learning tasks.

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing.