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5篇 您的检索式：作者名="Dixit Garg"

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		题名	作者	年代	出处	被引量
	1	Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis显示文摘	Somvir Singh Nain Dixit Garg Sanjeev Kumar	2018	Advances in Manufacturing2018，6，2：	3
	2	Complexation behaviour of benzilmuno(lepidyl)hydrazone (BLH) toward bivalent metal ions: A potentiometric study显示文摘	GARG B S CHOUDHURY M R DIXIT R	1989	Proc Indian Acad Sci1989，101，5：	1
	3	Levels of lipid peroxides and antioxidants in smokers and nonsmokers显示文摘	Garg N Singh R Dixit J	2006	J Periodont Res2006，41，：	1
	4	Development and evaluation of manually operated garlic planter显示文摘	Garg I K Anoop Dixit	2003	Agricultural Mechanization in Asia Africa and Latin America2003，34，2：	1
	5	ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning显示文摘Opportunistic Networks(OppNets)is gaining popularity day-by-day due to their various applications in the real-life world.The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication.Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks(Manets)and Wireless Sensor Networks(WSNs),therefore,it inherits most of the traits from both mentioned networking techniques.Apart from its popularity,Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network.These issues lower the overall performance of the network.Keeping this thing in mind,ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz.pattern prediction,decision tree prediction,adamic-adar method for complex networks.Apart from this,ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background.Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service(QoS)parameters.	Puneet Garg Ashutosh Dixit Preeti Sethi	2022	Computer Systems Science & Engineering2022，40，2：	0

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