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This paper introduces the intelligent interconnected healthcare system (IIHS), an innovative fusion of the Internet of Things (IoT) and big data analytics technologies designed to revolutionize proactive and personalized healthcare. IIHS facilitates the integration of real-time data from various devices, ambient sensors, and hospital equipment, creating a continuous stream of comprehensive healthcare data. Leveraging advanced data analysis, IIHS offers actionable insights for ongoing patient health monitoring, trend prediction through machine learning, and rapid information access via a user-friendly interface. The system architecture features a combination of centralized cloud storage and edge storage at healthcare facilities, enhancing both efficiency and security in data management. The effectiveness of IIHS has been demonstrated in two healthcare facilities, which reported significant reductions in patient length of stay and readmission rates. This indicates the system's potential to improve patient care while seamlessly integrating with existing healthcare infrastructures. IIHS represents the future of digital and personalized medicine, offering a scalable, patient-centric solution that supports the ongoing transformation towards data-driven healthcare. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics. [ABSTRACT FROM AUTHOR]

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem that belongs to a class of problems known as NP-hard, which is an exceptional case of a travelling salesman problem (TSP), which determines a set of routes enabling multiple salesmen to start at and return to home cities (depots). The penguins search optimization algorithm (PeSOA) is a new metaheuristic optimization algorithm. This paper presents a discrete penguins search optimization algorithm (PeSOA) for solving the multiple traveling salesman problem (MTSP). A set of benchmarks of TSP instances from TSPLIB library evaluates the PeSOA. The experimental results show that PeSOA is very efficient in finding the right solutions in a reasonable time. [ABSTRACT FROM AUTHOR]