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| 1 | Long-term management of gout: Nonpharmacologic and pharmacologic therapies 显示文摘 | Yashaar Chaichian Saima Chohan Michael' A Becker | 2014 | Rheum Dis Clin North Am2014，40，2： | 1 |
| 2 | Cloud-based software platform for big data analytics in smart grids 显示文摘 | YOGESH S SAIMA A ALOK K | 2013 | Computing in Science & Engineering2013，15，4： | 1 |
| 3 | Effect of different light regimens on performance of broilers显示文摘 | Mahmud A Saima Rafiullah | 2011 | Journal of Animal and Plant Science2011，21，1： | 1 |
| 4 | Use of a new video-laryngoscope (MeGRATH MAC) in patients with a difficult airway显示文摘 | Arai T Saima S Taguchi A | 2013 | Masui2013，62，9： | 1 |
| 5 | Synthesis and spectroscopic characterization of 2,3-epoxy/3-chloro-2-hydroxy propyl derivatives of quaternary ammonium salts:useful cationic fixing agents 显示文摘 | Saima S Saeed A | 2008 | Chinese Journal of Chemistry2008，26，： | 1 |
| 6 | Chcmomctric approach to validating faecal sterols as source tracer for faecal contamination in water 显示文摘 | Saima N Osmana R Spiana D R S A | 2009 | Water Research2009，43，20： | 1 |
| 7 | The effect of a six-month exercise program on very low-density lipoprotein apolipoprotein B secretion in type 2 diabetes显示文摘 | Saima A Michael S Cleire P | 2004 | J Clin Endocrino Metab2004，89，： | 1 |
| 8 | Effect of ly- sine supplementation in low protein diets on the perform- ance of growing broilers显示文摘 | Saima M Z U Jabbar M A Mehmud A | 2010 | Pak Vet J2010，30，： | 1 |
| 9 | Use of a new video- laryngoscope ( McGRATH MAC ) in patients with a difficult airway 显示文摘 | Arai T Saima S Taguchi A | 2013 | Masui2013，62，9： | 1 |
| 10 | In-vitro propagation of (Bougainvillea spectabilis) through shoot apex culture显示文摘 | JAVED A M SMD H SAIMA N | 1996 | Pak J Bot1996，28，2： | 1 |
| 11 | An antitumor pectic poly- saccharide from Feronia limonia显示文摘 | Saima Y Das A K Sarkar K K | 2000 | International Journal of Bio- logical Macromolecules2000，27，5： | 1 |
| 12 | 子痫前期和晚年患痴呆风险:全国的队列研究显示文摘目的探究子痫前期及晚年患痴呆的相关性,包括整体和不同类型的痴呆以及发病时间。方法基于全国登记的队列研究。背景丹麦。研究人群1978至2015年所有至少有一次活胎生产或死胎的女性。主要结局测量应用Cox回归模型计算比较有及没有子痫前期病史女性痴呆率的危害比(HR)。 | Saima Basit Jan Wohlfahrt Heather A Boyd 李洁(译) 毛晨晖(校) | 2019 | 英国医学杂志中文版2019，22，5： | 0 |
| 13 | Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions显示文摘Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 years.Many strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity prices.Fuzzy c-means(FCM)is one of the popular clustering approach for generating fuzzy membership functions.However,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian MF.The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions.As a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required.Therefore,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm.The approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is used.The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted. | Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan Said Jadid Abdulkadir | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 14 | Decline in subarachnoid haemorrhage volumes associated with the first wave of the COVID- 19 pandemic显示文摘Background During the COVID-19 pandemic,decreased volumes of stroke admissions and mechanical thrombectomy were reported.The study’s objective was to examine whether subarachnoid haemorrhage(SAH)hospitalisations and ruptured aneurysm coiling interventions demonstrated similar declines.Methods We conducted a cross-sectional,retrospective,observational study across 6 continents,37 countries and 140 comprehensive stroke centres.Patients with the diagnosis of SAH,aneurysmal SAH,ruptured aneurysm coiling interventions and COVID-19 were identified by prospective aneurysm databases or by International Classification of Diseases,10th Revision,codes.The 3-month cumulative volume,monthly volumes for SAH hospitalisations and ruptured aneurysm coiling procedures were compared for the period before(1 year and immediately before)and during the pandemic,defined as 1 March-31 May 2020.The prior 1-year control period(1 March-31 May 2019)was obtained to account for seasonal variation.Findings There was a significant decline in SAH hospitalisations,with 2044 admissions in the 3 months immediately before and 1585 admissions during the pandemic,representing a relative decline of 22.5%(95%CI−24.3%to−20.7%,p<0.0001).Embolisation of ruptured aneurysms declined with 1170-1035 procedures,respectively,representing an 11.5%(95%CI−13.5%to−9.8%,p=0.002)relative drop.Subgroup analysis was noted for aneurysmal SAH hospitalisation decline from 834 to 626 hospitalisations,a 24.9%relative decline(95%CI−28.0%to−22.1%,p<0.0001).A relative increase in ruptured aneurysm coiling was noted in low coiling volume hospitals of 41.1%(95%CI 32.3%to 50.6%,p=0.008)despite a decrease in SAH admissions in this tertile.Interpretation There was a relative decrease in the volume of SAH hospitalisations,aneurysmal SAH hospitalisations and ruptured aneurysm embolisations during the COVID-19 pandemic.These findings in SAH are consistent with a decrease in other emergencies,such as stroke and myocardial infarction. | Thanh N Nguyen Diogo C Haussen Muhammad M Qureshi Hiroshi Yamagami Toshiyuki Fujinaka Ossama Y Mansour Mohamad Abdalkader Michael Frankel Zhongming Qiu Allan Taylor Pedro Lylyk Omer F Eker Laura Mechtouff Michel Piotin Fabricio Oliveira Lima Francisco Mont'Alverne Wazim Izzath Nobuyuki Sakai Mahmoud Mohammaden Alhamza R Al-Bayati Leonardo Renieri Salvatore Mangiafico David Ozretic Vanessa Chalumeau Saima Ahmad Umair Rashid Syed Irteza Hussain Seby John Emma Griffin John Thornton Jose Antonio Fiorot Rodrigo Rivera Nadia Hammami Anna M Cervantes-Arslanian Hormuzdiyar H Dasenbrock Huynh Le Vu Viet Quy Nguyen Steven Hetts Romain Bourcier Romain Guile Melanie Walker Malveeka Sharma Don Frei Pascal Jabbour Nabeel Herial Fawaz Al-Mufti Atilla Ozcan Ozdemir Ozlem Aykac Dheeraj Gandhi Chandril Chugh Charles Matouk Pascale Lavoie Randall Edgell Andre Beer-Furlan Michael Chen Monika Killer-Oberpfalzer Vitor Mendes Pereira Patrick Nicholson Vikram Huded Nobuyuki Ohara Daisuke Watanabe Dong Hun Shin Pedro SC Magalhaes Raghid Kikano Santiago Ortega-Gutierrez Mudassir Farooqui Amal Abou-Hamden Tatsuo Amano Ryoo Yamamoto Adrienne Weeks Elena A Cora Rotem Sivan-Hoffmann Roberto Crosa Markus Möhlenbruch Simon Nagel Hosam Al-Jehani Sunil A Sheth Victor S Lopez Rivera James E Siegler Achmad Fidaus Sani Ajit S Puri Anna Luisa Kuhn Gianmarco Bernava Paolo Machi Daniel G Abud Octavio M Pontes-Neto Ajay K Wakhloo Barbara Voetsch Eytan Raz Shadi Yaghi Brijesh P Mehta Naoto Kimura Mamoru Murakami Jin Soo Lee Ji Man Hong Robert Fahed Gregory Walker Eiji Hagashi Steve M Cordina Hong Gee Roh Ken Wong Juan F Arenillas Mario Martinez-Galdamez Jordi Blasco Alejandro Rodriguez Vasquez Luisa Fonseca M Luis Silva Teddy Y Wu Simon John Alex Brehm Marios Psychogios William J Mack Matthew Tenser Tatemi Todaka Miki Fujimura Roberta Novakovic Jun Deguchi Yuri Sugiura Hiroshi Tokimura Rakesh Khatri Michael Kelly Lissa Peeling Yuichi Murayama Hugh Stephen Winters Johnny Wong Mohamed Teleb Jeremy Payne Hiroki Fukuda Kosuke Miyake Junsuke Shimbo Yusuke Sugimura Masaaki Uno Yohei Takenobu Yuji Matsumaru Satoshi Yamada Ryuhei Kono Takuya Kanamaru Masafumi Morimoto Junichi Iida Vasu Saini Dileep Yavagal Saif Bushnaq Wenguo Huang Italo Linfante Jawad Kirmani David S Liebeskind Viktor Szeder Ruchir Shah Thomas G Devlin Lee Birnbaum Jun Luo Anchalee Churojana Hesham E Masoud Carlos Ynigo Lopez Brendan Steinfort Alice Ma Ameer E Hassan Amal Al Hashmi Mollie McDermott Maxim Mokin Alex Chebl Odysseas Kargiotis Georgios Tsivgoulis Jane G Morris Clifford J Eskey Jesse Thon Leticia Rebello Dorothea Altschul Oriana Cornett Varsha Singh Jeyaraj Pandian Anirudh Kulkarni Pablo M Lavados Veronica V Olavarria Kenichi Todo Yuki Yamamoto Gisele Sampaio Silva Serdar Geyik Jasmine Johann Sumeet Multani Artem Kaliaev Kazutaka Sonoda Hiroyuki Hashimoto Adel Alhazzani David Y Chung Stephan A Mayer Johanna T Fifi Michael D Hill Hao Zhang Zhengzhou Yuan Xianjin Shang Alicia C Castonguay Rishi Gupta Tudor G Jovin Jean Raymond Osama O Zaidat Raul G Nogueira SVIN COVID-19 Registry,the Middle East North Africa Stroke and Interventional Neurotherapies Organization(MENA-SINO),Japanese Society of Vascular and Interventional Neurology Society(JVIN) | 2021 | Stroke & Vascular Neurology2021，6，4： | 0 |
| 15 | Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification显示文摘Mental health signifies the emotional,social,and psychological well-being of a person.It also affects the way of thinking,feeling,and situation handling of a person.Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life.Mental health problems are rising globally and constituting a burden on healthcare systems.Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated.The machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric nosology.This research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and Schizophrenia.These challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models.Furthermore,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical experts.The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique.This proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%respectively.This framework seems suitable in the case of huge and more diverse multiclass datasets.Furthermore,three vector spaces based on TF-IDF(unigram,bi-gram,and tri-gram)are also tested on the machine learning models and the proposed model. | Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi | 2022 | Computers, Materials & Continua2022，，9： | 0 |
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