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Conditional Generative Adversarial Network Approach for Autism Prediction

Computer systems science & Engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja s.kannimuthu Department of Computer science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism spectrum Disorder(AsD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect AsD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

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Context-Aware Practice Problem Recommendation Using Learners’ skill Level Navigation Patterns

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Intelligent Automation & soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh s.kannimuthu Computer science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

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