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您的检索式：作者名="Muhammad Haroon Nadeem"
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| 1 | Anti-inflammatory activities of Sieboldogenin from Smilax china Linn.: Experimental and computational studies显示文摘 | Inamullah Khan Muhammad Nisar Farooq Ebad Said Nadeem Muhammad Saeed Haroon Khan Samiullah Fazli Khuda Nasiara Karim Zia Ahmad | 2008 | Journal of Ethnopharmacology2008，，1： | 2 |
| 2 | A hybrid circuit breaker with fault current limiter circuit in a VSC-HVDC application显示文摘A conventional hybrid circuit breaker(HCB)is used to protect a voltage source converter-based high voltage direct current transmission system(VSC-HVDC)from a short circuit fault.With the increased converter capacity,the DC protection equipment also requires a regular upgrade.This paper adopts a novel type of HCB with a fault current limiter circuit(FCLC),and focuses on the responses of voltage and current during DC faults,which are associated with parameter selection.PSCAD/EMTDC based simulation of a three-terminal VSC-HVDC system confirms the effectiveness and value of HCB with FCLC,by using an equivalent circuit modelling approach.Laboratory experimental tests validate the simulation results.The peak fault current is reduced according to the current limiting inductor(CLI)increase,and can be isolated more quickly.By adopting parallel metal oxide arrester(MOA)with the main branch of HCB,voltage stresses across the breaker components decrease during transient and continuous operation,and less energy needs to be dissipated by the MOA.The remnant current for all cases is transmitted to power dissipating resis-tor(PDR)in the final stage,and the fault current is reduced to the lowest possible value.When the current from the main branch is transferred to the FCLC branch,transient voltage spikes occur,while smaller PDR is required to absorb current in the final stage. | Muhammad Ahmad Chunyang Gong Muhammad Haroon Nadeem Hui Chen Zhixin Wang | 2022 | Protection and Control of Modern Power Systems2022，7，1： | 1 |
| 3 | Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations显示文摘Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services. | Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain | 2023 | Computers, Materials & Continua2023，，3： | 1 |
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