Sensor Controlled Sanitizer Door Knob with Scan Technique

Increased agricultural activity is intended as intelligent agriculture or precision agriculture to be of critical importance. The fast growth of networking has resulted in IoT-based farm management systems. Increased agricultural activity is intended as intelligent agriculture or precision agriculture to be of critical importance. The established cloud-based platforms cannot cope with the huge quotas and diverse data provided by the connected IoT devices, which are based on conventional cloud models. It is important to get data processing closer to the origins of their output to reduce lag in aiding real-time decision-making based on the data generated. The adoption of fog-based models will solve this and will be discussed in this chapter. To ensure optimum bandwidth usage and low latency for real-time decision-making, an IoT-Fog farm management system may be more capable.

show abstract

Fog-IoT-Assisted-Based Smart Agriculture Application

Whig¹,

Kouser

Velu³

et al. 2022

Advances in Computational Intelligence and Robotics

View full text Add to dashboard Cite

show abstract

Adaptive Clinical Treatments and Reinforcement Learning for Automatic Disease diagnosis

Whig¹,

Gupta

Jiwani

et al. 2022

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

Machine learning models are taught how to make a series of decisions depending on a set of inputs in reinforcement learning. The agent learns how to accomplish a goal in an unexpected, maybe complex environment. Reinforcement learning places artificial intelligence in a game-like environment. It solves the problem by trial and error. Artificial intelligence is rewarded or punished based on its actions. Its purpose is to maximize the amount of money paid out in total. In addition to providing the game's rules, the designer does not give any feedback or recommendations on how to win the model. To maximize reward, the model must determine the optimum way to do a job, beginning with purely random trials and progressing to complex techniques and superhuman abilities. Reinforcement learning, with its power of search and diversity of trials, is likely the most effective strategy for hinting at a system's originality. Unlike humans, AI can learn from thousands of concurrent gameplays if a reinforcement learning algorithm is run on sufficiently efficient computer infrastructure.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Sensor Controlled Sanitizer Door Knob with Scan Technique

Cited by 2 publications

References 3 publications

Fog-IoT-Assisted-Based Smart Agriculture Application

Fog-IoT-Assisted-Based Smart Agriculture Application

Adaptive Clinical Treatments and Reinforcement Learning for Automatic Disease diagnosis

Contact Info

Product

Resources

About