StuA: An Intelligent Student Assistant

With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter is more effective compared to the standard ACS

Intelligent electronic design for mechatronic systems

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

利用者の行動パターンの検知を通じた機密情報とコミュニケーションの内部統制に関する研究

国立大学法人長岡技術科学大

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization