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您的检索式：作者名="Naveed Jan"
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| 1 | Novel mutations in PDE6A and CDHR1 cause retinitis pigmentosa in Pakistani families显示文摘AIM:To investigate the genetic basis of autosomal recessive retinitis pigmentosa(arRP)in two consanguineous/endogamous Pakistani families.METHODS:Whole exome sequencing(WES)was performed on genomic DNA samples of patients with arRP to identify disease causing mutations.Sanger sequencing was performed to confirm familial segregation of identified mutations,and potential pathogenicity was determined by predictions of the mutations’functions.RESULTS:A novel homozygous frameshift mutation[NM_000440.2:c.1054delG,p.(Gln352Argfs*4);Chr5:g.149286886del(GRCh37)]in the PDE6A gene in an endogamous family and a novel homozygous splice site mutation[NM_033100.3:c.1168-1G>A,Chr10:g.85968484G>A(GRCh37)]in the CDHR1 gene in a consanguineous family were identified.The PDE6A variant p.(Gln352Argfs*4)was predicted to be deleterious or pathogenic,whilst the CDHR1 variant c.1168-1G>A was predicted to result in potential alteration of splicing.CONCLUSION:This study expands the spectrum of genetic variants for arRP in Pakistani families. | Muhammad Dawood Siying Lin Taj Ud Din Irfan Ullah Shah Niamat Khan Abid Jan Muhammad Marwan Komal Sultan Maha Nowshid Raheel Tahir Asif Naveed Ahmed Muhammad Yasin Emma LBaple Andrew HCrosby Shamim Saleha | 2021 | International Journal of Ophthalmology(English edition)2021，14，12： | 0 |
| 2 | Text Augmentation-Based Model for Emotion Recognition Using Transformers显示文摘Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations. | Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 3 | Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network显示文摘Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level. | Muhammad Rizwan Sana Ul Haq Noor Gul Muhammad Asif Syed Muslim Shah Tariqullah Jan Naveed Ahmad | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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