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- 저자 : Afzal Hussain Shahid (검색결과 3 건)
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A deep learning approach for prediction of Parkinson’s disease progression
Afzal Hussain Shahid,Maheshwari Prasad Singh 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.2
This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson’s telemonitoringdataset to predict Parkinson’s disease (PD) progression. PD is a chronic and progressive nervous system disorder that aff ectsbody movement. PD is assessed by using the unifi ed Parkinson’s disease rating scale (UPDRS). In this paper, fi rstly, principalcomponent analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset andto reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN modelwith a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predictingMotor and Total-UPDRS score. The model’s performance is evaluated by conducting several experiments and the result iscompared with the result of previously developed methods on the same dataset. The model’s prediction accuracy is measuredby fi tness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coeffi cient of determination (R 2 ). The MAE, RMSE, and R 2 values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221,and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and effi cacy of the proposed method for predicting the UPDRS score in PD progression.
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A Step towards the Improvement in the Performance of Text Classification
( Shahid Hussain ),( Muhammad Rafiq Mufti ),( Muhammad Khalid Sohail ),( Humaira Afzal ),( Ghufran Ahmad ),( Arif Ali Khan ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier’s performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.
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Iqra Mir,Sania Aamir,Syed Rizwan Hussain Shah,Muhammad Shahid,Iram Amin,Samia Afzal,Amjad Nawaz,Muhammad Umer Khan,Muhammad Idrees 질병관리본부 2022 Osong Public Health and Research Persptectives Vol.13 No.2
The coronavirus disease 2019 (COVID-19) pandemic rapidly spread globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is a positive-sense single-stranded RNA virus with a reported fatality rate ranging from 1% to 7%, and people with immune-compromised conditions, children, and older adults are particularly vulnerable. Respiratory failure and cytokine storm-induced multiple organ failure are the major causes of death. This article highlights the innate and adaptive immune mechanisms of host cells activated in response to SARS-CoV-2 infection and possible therapeutic approaches against COVID-19. Some potential drugs proven to be effective for other viral diseases are under clinical trials now for use against COVID-19. Examples include inhibitors of RNA-dependent RNA polymerase (remdesivir, favipiravir, ribavirin), viral protein synthesis (ivermectin, lopinavir/ritonavir), and fusion of the viral membrane with host cells (chloroquine, hydroxychloroquine, nitazoxanide, and umifenovir). This article also presents the intellectual groundwork for the ongoing development of vaccines in preclinical and clinical trials, explaining potential candidates (live attenuated-whole virus vaccines, inactivated vaccines, subunit vaccines, DNA-based vaccines, protein-based vaccines, nanoparticle-based vaccines, virus-like particles and mRNA-based vaccines). Designing and developing an effective vaccine (both prophylactic and therapeutic) would be a long-term solution and the most effective way to eliminate the COVID-19 pandemic.
ScienceON
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