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7篇 您的检索式：作者名="Arpitha V"

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		题名	作者	年代	出处	被引量
	1	A sub- set of human limbal epithelial cells with greater nucleus-to-cyto- plasm ratio expressing high levels of p63 possesses slow-cycling property 显示文摘	Arpitha P Prajna NV Srinivasan M Muthukkaruppan V	2008	Cornea2008，27，10：	1
	2	High expression of p53 combined with a large N/C ratio defines a subset of human limbal epithelial cells: implications on epithelial stem cells显示文摘	Arpitha P Prajna NV Srinivasan M Muthukkaruppan V	2005	Invest Ophthalmol Vis Sci2005，46，10：	1
	3	Comparative study on ECG data compression methods 显示文摘	ABHINAND1NI U ARPITHA V MADHURI C R VIJAY V	2013	International Journal of Innovative Research and Development2013，2，41：	1
	4	A Subset of Human Limbal Epithelial Cells With Greater Nucleus-to-Cytoplasm Ratio Expressing High Levels of p63 Possesses Slow-Cycling Property显示文摘	Parthasarathy Arpitha Namperumalsamy V Prajna Muthiah Srinivasan Veerappan Muthukkaruppan	2008	Cornea2008，，10：	1
	5	High expression of p63 combined with a large N/C ratio defines a subset of human limbal epithelial cells: implications on epithelial stem cells显示文摘	Arpitha P Prajna N V Srinivasan M	2005	Invest Ophthaltool Vis Sci2005，46，10：	1
	6	High expression of p63 combined with a large N/C ratio defines a subset of human limbal epithelial cells: implications on epithelial stern cells 显示文摘	Arpitha P Prajna N V Srinivasan M	2005	Invest Ophthalmol Vis Sei2005，46，：	1
	7	User preference-based intelligent road route recommendation using SARSA and dynamic programming显示文摘Traffic congestion is one of the main challenges in transportation engineering. It directly impactsthe economy by increasing travel time and affecting the environment by excessive fuel consumptionand emission. Road route recommendation to overcome the congestion by alternativeroute suggestions has gained high importance. The existing route recommendation systems areproposed using the reinforcement learning algorithm (Q-learning). The techniques suggestedin this paper are state-action-reward-state-action (SARSA) algorithm and dynamic programming(DP) to guide the commuters to reach the destination with an optimal solution. The algorithmconsiders travel time, cost, flexibility, and traffic intensity as the user preference attributes torecommend an optimal route. The recommended system is implemented by building a roadnetwork graph. We assign values to each user preference attribute along the edges, which cantake high(1) or low(0) values. By considering these values, the system recommends the route.The proposed system performance is evaluated based on computation time, cumulative reward,and accuracy. The results show that DP outperforms the SARSA algorithm.	Roopa Ravish Shanta Rangaswamy Arpitha V Vasuprada U	2023	Journal of Control and Decision2023，10，3：	0

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