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| 1 | Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects显示文摘Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects identification.While extensive research has identified explicit aspects,little effort has been put forward on implicit aspects extraction due to the complexity of the problem.Moreover,existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’dependency problems.Therefore,in this paper,a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed.The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF(Bidirectional Long Short Memory-Conditional Random Field),which serve as a memory to process dependent sentences to infer implicit aspects.It can identify implicit aspects from four types of sentences,including independent and three types of dependent sentences.The study is evaluated on a largemovie reviews dataset with 50k examples.The experimental results showed that the explicit aspect identification method achieved 89%F1-score and implicit aspect extraction methods achieved 76%F1-score.In addition,the proposed approach also performs better than the state-of-the-art techniques(NMFIAD andML-KB+)on the product review dataset,where it achieved 93%precision,92%recall,and 93%F1-score. | Jibran Mir Azhar Mahmood Shaheen Khatoon | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 2 | A comparative study of student satisfaction lev- el in distance learning and live classroom at higher education level显示文摘 | Azhar MAHMOOD Sheikh Tariq Mahmood & Allah Bakhsh MALIK | 2012 | Turkish Online Journal of Distance Education (TO- JDE)2012，13，1： | 1 |
| 3 | Phytochemical studies on the seed extract of Piper nigrum Linn.显示文摘 | Bina S. Siddiqui Tahsin Gulzar Azhar Mahmood Sabira Begum Bushra Khan Munawwer Rasheed Farhana Afshan Rajput M. Tariq | 2005 | Natural Product Research2005，，7： | 1 |
| 4 | Room Temperature Ferromagnetism in Ga1-xHoxN (x=0.0 and 0.05) Diluted Magnetic Semiconductor Thin Films显示文摘做的钬轧了冲淡的磁性的半导体薄电影被退火的热蒸发技术和随后的氨准备了。X 光检查衍射大小表明所有山峰属于完全六角形的 wurtzite 结构。表面形态学和作文分析被扫描电子显微镜学和精力执行散光谱学分别地。房间温度 Ga1xHoxN 的铁磁性的性质(x=0.0, 0.05 ) 电影用颤动被分析在房间温度的样品磁强计。磁性的大小证明 undoped 拍摄(即轧) 展出抗磁的行为,当惊讶做(Ga0.95Ho0.05N ) 电影展出了铁磁性的行为时。 | Ghulam Murtaza Rai Muhammad Azhar Iqbal Yong-bing Xu lain Gordon Will Qasim Mahmood | 2012 | Chinese Journal of Chemical Physics2012，25，3： | 0 |
| 5 | Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)显示文摘Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes. | Sadaf Qazi Muhammad Usman Azhar Mahmood Aaqif Afzaal Abbasi Muhammad Attique Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，1： | 0 |
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