Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19

Devi, S. Aicevarya; Felix, A.; Narayanamoorthy, Samayan; Kalaiselvan, Samayan; Balaenu, Dumitru; Ahmadian, Ali

doi:10.1016/j.orp.2022.100258

Cited by 12 publications

(6 citation statements)

References 38 publications

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“…As rendered from the available observational history, the da Vinci Surgical System was introduced to conduct robotic surgery. The telepresence Coordinated Care Robot is one of the modern-day applications of robotic medicine delivery systems that accept and transports medication safely to the beds of needy patients [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

Senthilkumar Selvaraj

2024

jes

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In this paper, we introduce an IoT-based robotic medicine delivery system designed to boost safety and efficiency in hospital settings. This IoT-embedded robotic system integrates advanced sensors such as proximity, temperature, pressure, pulse rate, etc., sensors for ensuring real-time health and environmental monitoring behind this system. The robot is connected to a centralised controller, enabling healthcare workers to monitor patients using a cloud-based system. . Our robotic system moves about with the support of both line follower and LiDAR scanning technology for environmental safety and proximity detection. Experimental results show that the robotic system was able to navigate static obstacles and dynamically move away from the mobile entities within its reach without touching them. Besides, the proposed robotic system was able to reach the bed at the right time under normal operating conditions, which shows the efficiency of this approach in real-time settings. Patient feedback suggests that patients are very happy about the system, in terms of its usefulness, trustworthiness, and to their suprise to be taken care of by a robot. Overall, our study shows the potential of the proposed robotic system in streamlining the existing medicine delivery process in hospitals. It can potentially improve various aspects including safety, efficiency, patient compliance, and quality of care delivery. The promising results suggest further studies for incorporating advanced features and for ensuring compliance with regulatory requirements. Additional studies are to be conducted to further improve the proposed sensor-based robotic medicine delivery system, to facilitate its adoption in hospitals.

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Section: Introductionmentioning

confidence: 99%

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

Senthilkumar Selvaraj

2024

jes

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“…As the technology matures, it promises to reduce traffic accidents caused by human error, alleviate congestion through optimized routing, and provide mobility solutions for those unable to drive. [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

S Devikala

2024

jes

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The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

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“…Among the decision making models, MCDM is one of the most widely used decision methodologies involving both quantitative and qualitative factors in which the ranking of the alternatives are found in relation with various criteria. This technique includes several well-known methods like TOPSIS, 5 VIKOR, 6 AHP, 7,8 COPRAS, 9 Entropy, 10 WASPAS, 11 DEMATEL, [12][13][14] MAUT, 15 etc. Thus, MCDM methods were used in the fuzzy field to select the appropriate robot for a particular application.…”

Section: Introductionmentioning

confidence: 99%

An integrated intuitionistic dense fuzzy Entropy-COPRAS-WASPAS approach for manufacturing robot selection

Sampathkumar

Felix

Kaabar

et al. 2023

Advances in Mechanical Engineering

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Manufacturing robots are used for industrial purposes. Robots used for manufacturing purpose have the power to create products from raw materials and are capable of operating endlessly even in lights-out situations for continuous production. Manufacturing robots can be used in applications like arc welding, spot welding, materials handling, machine tending, painting, machine cutting and so on. Selecting a robot for a given application is complex. However, robot selection is used in selecting a suitable robot for the preferred output with specific application ability. In recent years, many authors used multi-criteria decision-making methods to select a robot that meets the need. This study presents a hybrid model by integrating complex proportional assessment (COPRAS) with the weighted aggregates sum product assessment (WASPAS) methods in intuitionistic dense fuzzy set to select a manufacturing robot for a particular application. Intuitionistic dense fuzzy entropy is used in calculating the weights for the criteria and intuitionistic dense fuzzy COPRAS and WASPAS are used in ranking the best alternatives where in the optimum types of robots are obtained. Here, intuitionistic dense fuzzy sets are used as it is capable of dealing with the intangible factors while selecting a robot. On the basis of the manufacturing robot selection, two comparisons are given. First, the results are compared with various [Formula: see text] values between 0 and 1 and second the results are compared with the fuzzy set, intuitionistic fuzzy set and the dense fuzzy set to show the effectiveness of the proposed methodology with the intuitionistic dense fuzzy set.

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Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19

Cited by 12 publications

References 38 publications

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

An integrated intuitionistic dense fuzzy Entropy-COPRAS-WASPAS approach for manufacturing robot selection

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