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Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users’ interests and generate product suggestions for the active users. Recommender systems are generally divided into three groups: Content-based, Knowledge-based, and collaborative-based, and in some cases hybrid. The main idea of collaborative filtering is that they predict a user’s interest in new items based on the recommendations of other people with similar interests. This Approach does not require having knowledge about items. Collaborative filtering has two main types: Memory-based and Model-based. Memory based Collaborative filtering makes use of user rating dataset to compute similarity index between set of users or set of items. The main purpose of this article is to offer a Memory-based Collaborative recommender system in order to optimize the results of Collaborative filtering algorithm. In the proposed method, the combination of fuzzy Grey Wolf Optimizer algorithm and Lion Optimization Algorithm is used to find the most similar users to the target user. The results of the proposed method confirmed a significant increment in Precision, Recall and F-measure in comparison with baseline methods.

The increasing growth of space achievements, which is the result of the NPD and the increase in the level of exploitation of its findings, has made the space sector an effective role in economic development and increasing GDP and the space industry is also subject to this category. Structural actions in space have created problems in the field of rockets and satellites as a concern in space over the past decade. A review of the research background reveals the impact of three levels of industry, firm and process on NPD. In this research, the problematic situation in the space industry was identified, through interviews and documentation, and the results were validated by creating a focus group. 34 factors related to NPD at this level were identified by reviewing the research background. The extent of their impact on achieving the country's space goals was assessed by the SAW method of MADM and five factors affecting NPD were selected. Then, the interaction of factors was evaluated with Micmac software. Relationships between factors were prioritized by structural analysis of factors. It was found that the factor of structure and organizational relationships at the level of the space industry has a direct impact on the time of new product entry into the market. Therefore, the rulers of the space sector should pay special attention to them. Finally, priorities and suggestions for improving decisions for NPD were presented.

Smart transport is an indispensable necessity in today's smart cities. In this thesis, six scenarios were implemented based on intelligent transportation. In the first scenario, the speed control process was implemented. In the second and third scenarios, the process of traffic planning was even and odd. In the fourth scenario, the vehicle relief system was implemented. In the fifth scenario, an emergency relief system was implemented. In the sixth scenario, the message was executed at the time of the car theft. The message delivery process has been fully implemented in the smart city area so that all vehicles in the smart transport process receive the message. An access point is called an access point whose task is to send a message. Given that the number of messages sent to the network decreases, there are two advantages to the low cost of sending messages as well as full coverage. So instead of sending the message directly to each car individually, the messages are sent to the roadside equipment and then sent to the vehicles passing the road through the roadside equipment. This roadside equipment is called fixed nodes. Certainly, sending messages to all these fixed nodes is not optimal and has two major drawbacks. The first disadvantage is that a car may cross several fixed target nodes and receive all of these fixed target nodes of the message. Also the second problem is that the number of messages received creates terrible statistics and imposes a computational overhead. So the solution to this problem is to select some of these fixed nodes as the target constant node. Only send the message to fixed target nodes and send it to the cars passing by. The first challenge of this thesis is the selection of these target constant nodes, which is implemented using the fuzzy ranked idealized programming algorithm. After solving the first challenge due to the communication board problem in the car network, the challenge of routing the message from the access point to the fixed node is raised. The solution to this challenge is computed using the Dijkstra algorithm and compared with the eTGMD algorithm in terms of message delivery rate, delivery delay, number of packets used, and number of fixed target nodes. The results of the proposed algorithm show a good and optimal improvement over the eTGMD algorithm.