Agents are software programs that perform tasks on behalf of others and they are used to clean the text data with their characteristics. Agents are task oriented with the ability to learn by themselves and they react to the situation. Learning characteristics of an agent is done by verifying its previous experience from its knowledgebase. An agent concept is a complementary approach to the Object Oriented paradigm with respect to the design and implementation of the autonomous entities driven by beliefs, goals and plans. Preference based text data cleaning is based on the selection issue. Preferences are given by the user in the form of alphabets, numbers and special characters.Preference based Text data cleaning process transforms the given text data into structured database and extracts the required information using the given keyword. Agents incorporated in the architectural design of a Text data cleaning process combines the features of Multi-Agent System (MAS) Framework, MAS with Learning (MAS-L) Framework. MAS framework reduces the development time and the complexity of implementing the software agents. MAS-L framework incorporates the intelligence and learning properties of agents present in the system. MAS-L Framework makes use of the Decision Tree learning and an evaluation function to decide the next best decision that applies to the machine learning technique.

Machine Learning algorithms have a variety of important applications, and among them, Recommender systems are crucial. The internet hosts an extensive volume of information, making it challenging for users to navigate and find relevant content. Recommender systems have therefore emerged as valuable tools to bridge this gap. They facilitate the connection between users and relevant content by offering personalized recommendations. In recent years personalized recommendation service has become a hotspot of web technology, and is widely used in information, shopping, film and television, etc. [1] . Recommender systems have been proved to be an important response to the information overload problem [17] . In this research paper, we describe our approach for a Movie Recommender System Utilizing Mean Reversion via the Bollinger Bands formulae. Collaborative filtering is a popular technique used in Recommender systems. However, it poses a challenge in the form of the cold start issue, where new users are added to the system without any ratings, and the filter is unable to offer useful recommendations due to a lack of understanding of their preferences. Similarly, newly released movies without any ratings also suffer from the same issue, leading to recommendations reinforcing themselves. To address this challenge, we incorporated the concept of Mean Reversion, which is a fundamental component of Natural Mathematics. Mean Reversion helps in mitigating the cold start issue by bringing new users and newly released movies into the fold of the Recommender system. Mean reversion is a statistical concept that refers to the tendency of a series of values to return to its longterm average after experiencing temporary fluctuations. In the context of Recommender systems, Mean Reversion can be used to address the cold start issue by estimating the average rating for a movie and adjusting it based on a new user's preference. This technique can help improve the accuracy of recommendations, particularly for new users and newly released movies that lack sufficient data.

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

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