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ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Computers, Materials & Continua 2022年第4期71卷 215-230页

作者： talha imtiaz baig Yaser Daanial Khan talha Mahboob Alam Bharat Biswal Hanan Aljuaid Durdana Qaiser Gillani School of Science and Technology University of Management and TechnologyLahorePakistan Department of Computer Science and Information Technology Virtual University of PakistanLahorePakistan Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdul Rahman University(PNU)RiyadhSaudi Arabia Department of Economics University of LahoreLahorePakistan

Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and *** is connected with many biological processes and closely linked with many metabolic *** develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this ***,most of the techniques and different traditional experimental models have a very high *** are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation *** study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset *** the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of ***,the predictor model is a very useful and helpful tool for lipoylation sites *** of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature *** wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

关键词： Lipoylation lysine feature vector post translational modification amino acid Mathew’s correlation coefficient neural network

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran talha imtiaz baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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