Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Internet of things (IoT), also referred to as the internet of objects, is a dynamic global information network consisting of internet - connected objects. Wireless sensor networks (WSN) and RFID enabled Internet of Things finds a plethora of applications in almost all the fields such as education, health, agriculture, transportation and entertainment. Compact smart devices constitute an essential part of IoT. These compact smart devices range widely in size, energy, use, capacity and computation power. On the other hand, the integration of these smart things into the standard internet introduces several security challenges because the majority of internet technologies and protocols were not designed to support IoT. Though number of researchers have explored about such security challenges and open problems in IoT, there is a lack of a systematic study of the security challenges in the IoT landscape. This paper briefs the motivation for IoT, secured IoT layered architecture, IoT applications, Security issues with various attacks in each layer of the IoT and existing methods for providing security solutions and their limitations.

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