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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Multi‐task framework of precipitation nowcasting

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1350-1363页

作者： Zheng Zhang Chuyao Luo Baoquan Zhang Hao Jiang Bowen Zhang Department of Computer Science Shenzhen Key Laboratory of Internet Information CollaborationHarbin Institute of TechnologyShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina

Precipitation forecasting plays an important role in disaster warning,agricultural production,and other *** solve this issue,some deep learning methods are proposed to forecast future radar echo images and convert them into rainfall *** spatiotemporal sequence prediction methods are usually based on a ConvRNN structure that combines a Convolutional Neural Network and Recurrent Neural ***,these existing methods ignore the image change prediction,which causes the coherence of the predicted image has ***,these approaches mainly focus on complicating model structure to exploit more historical spatiotemporal ***,they ignore introducing other valuable information to improve *** tackle these two issues,we propose GCMT‐ConvRNN,a multi‐ask framework of *** for precipitation nowcasting as the main task,it combines the motion field estimation and sub‐regression as auxiliary *** this framework,the motion field estimation task can provide motion information,and the sub‐regression task offers future ***,to reduce the negative transfer between the auxiliary tasks and the main task,we propose a new loss function based on the correlation of gradients in different *** experiments show that all models applied in our framework achieve stable and effective improvement.

关键词： deep neural networks motion estimation

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Hybrid Graph Neural Networks with LSTM Attention Mechanism for Recommendation Systems in MOOCs 20

Hybrid Graph Neural Networks with LSTM Attention Mechanism f...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Luo, Hao Abdipoor, Sina Li, Qing Xu, Litao BaoShan University Department Of Big Data BaoShan67800 China Universiti Putra Malaysia Upm Department Of Computer Science Serdang43400 Malaysia

ISBN: (纸本)9798350382310

Massive Open Online Courses (MOOC) represent a relatively recent development in the educational landscape, rapidly gaining popularity and drawing research attention. Transforming the traditional approach to education, MOOCs provide learners with flexible and accessible avenues for acquiring knowledge. However, the sheer abundance of courses available can overwhelm users. Recommending relevant courses to learners remains a complicated challenge, impacting engagement and completion rates. Conventional recommendation systems often struggle to capture MOOCs' dynamic and interconnected nature. This paper examines the application of Hybrid Graph Neural Networks with a Long Short-Term Memory attention mechanism (HGNN-LSTM) to enhance recommendation systems for MOOCs. By leveraging learner behavior data, these approaches examine and predict learning activities, discern temporal relationships among courses through Adam optimization algorithms, and ultimately enhance the accuracy of recommendations. We illustrate that HGNN-LSTM adeptly captures hidden linkages between courses, resulting in improved automatic course classification and a reduction in the burden of course maintenance. The paper concludes with an analysis of challenges, identified gaps, and suggestions for potential future research directions. © 2024 IEEE.

关键词： Recommender systems

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Analysis of Bitcoin Prediction using Transparent and Opaque Machine Learning Algorithms 15

Analysis of Bitcoin Prediction using Transparent and Opaque ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Singh, Kavita R. Jamar, Anushri V. Khadgi, Pracheta P. Kapse, Smita Kumar, Ajay Department of Computer Technology Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India School of Big Data and Computer Science Guizhou Province Guiyang City China

ISBN: (纸本)9798331300579

The unpredictable nature of cryptocurrency markets, particularly Bitcoin, has attracted significant attention from investors, researchers, and financial institutions seeking to understand and predict price movements. This study aims to analyse the effectiveness of both transparent and opaque machine learning algorithms in predicting Bitcoin prices. Transparent algorithms, such as linear regression and decision trees, provide clear insights into the factors influencing predictions. On the other hand, opaque algorithms, like ensemble methods, offer complex, non-linear models that may capture intricate patterns in the data. The main analysis is to lower the mean absolute error and enhance the r2 square, both key indicators of forecast accuracy. The study presents that linear regression and random forest outperformed others, achieving the smallest error and highest variance explanation, suggesting their effectiveness in Bitcoin price prediction with and without outliers respectively. In contrast, the support vector machine underperforms compared to all other machine learning algorithms under consideration. This paper sheds light on the most capable machine-learning techniques for Bitcoin forecasting, aiding trader investment choices and guiding academic study in the field of prediction. © Grenze Scientific Society, 2024.

关键词： Prediction models

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS 12

TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Regression analysis

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In the Eyes of the Bystander: Are the Stances on Different Conflicts Correlated?

In the Eyes of the Bystander: Are the Stances on Different C...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tao, Yiyao Zhang, Hengyu Dey, Babli Tulga, Selenge Lyu, Hanjia Luo, Jiebo University of Rochester Goergen Institute for Data Science RochesterNY United States University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350362480

Public opinion on international conflicts, such as the concurrent Russia-Ukraine and Israel-Palestine crises, often reflects a society's values, beliefs, and history. These simultaneous conflicts have sparked heated global online discussions, offering a unique opportunity to explore the dynamics of public opinion in multiple international crises. This study investigates how public opinions toward one conflict might influence or relate to another, a relatively unexplored area in contemporary research. Focusing on Chinese netizens, who represent a significant online population, this study examines their perspectives, which are increasingly influential in global discourse due to China's unique cultural and political landscape. The research finds a range of opinions, including an overall neutral stance towards both conflicts and a statistical correlation between attitudes towards each, indicating interconnected or mutually influenced viewpoints. The study also highlights the significant role of news media in impacting public opinion, particularly in China, where state policies and global politics shape conflict portrayal. © 2024 IEEE.

关键词： opinion mining social media stance detection

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Evaluating the Usability and Career Guidance Functionality of an Academic Performance Tracking Application Using the UEQ 10

Evaluating the Usability and Career Guidance Functionality o...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Mukaromah, Siti Pratama, Arista Wahanani, Henni Endah Idhom, Mohammad Agussalim Safitri, Eristya Maya University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Informatics Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Data Science Surabaya Indonesia

ISBN: (纸本)9798331521295

This article evaluates an academic application designed to provide career guidance using the User Experience Questionnaire (UEQ). A survey involving 255 respondents assessed the application across 6 UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The results reveal the highest scores on Attractiveness 1.473 and Perspicuity 1.642, indicating that the platform is visually appealing and easy to navigate. However, lower scores on Novelty 0.774 and Dependability 1.243 highlight areas requiring improvement, particularly in building trust among users and introducing innovative features. The implications of these findings suggest that while the platform holds significant potential as a tool for academic monitoring and career guidance, enhancing user engagement and reliability will be critical to achieving broader acceptance. Addressing the gaps in Stimulation (1.135) and Novelty could lead to a more engaging and unique user experience, aligning with the evolving needs of students. This study provides actionable insights for the iterative development of user-centered educational tools, emphasizing the importance of UX in supporting informed decision-making and improving academic outcomes. © 2024 IEEE.

关键词： Decision making

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Effort-aware cross-project just-in-time defect prediction framework for mobile apps

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Frontiers of computer science 2022年第6期16卷 15-29页

作者： Tian CHENG Kunsong ZHAO Song SUN Muhammad MATEEN Junhao WEN School of Big Data and Software Engineering Chongqing UniversityChongqing401331China School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Air University Multan CampusMultan60000Pakistan

As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new ***-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile *** the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project *** this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile *** specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative ***,the adversarial learning technique is used to extract the common feature embedding for the model *** conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance *** results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

关键词： kernel-based principal component analysis adversarial learning just-in-time defect prediction cross-project model

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Improving AI-Based Skin Disease Classification with StyleGAN3 for Minority Skin Tone Generation 16

Improving AI-Based Skin Disease Classification with StyleGAN...

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16th Biomedical Engineering International Conference, BMEiCON 2024

作者： Tanantong, Tanatorn La-Or-On, Pangon Srijiranon, Krittakom Thammasat University Research Thammasat University Unit in Data Innovation and Artificial Intelligence Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798331505431

Skin diseases are prevalent in Thailand due to the hot temperatures, humid climate, and increasing diversity of skin tones, which complicates diagnosis. Artificial intelligence (AI) has improved skin disease detection through deep learning models, aiding faster diagnoses and reducing the workload on dermatologists. However, AI models often struggle with underrepresentation of darker skin tones in training datasets, affecting performance. To address this imbalance, data augmentation techniques like generative models are recommended. This research enhances skin disease classification using StyleGAN3 for data augmentation to balance minority skin tone classes. The Fitzpatrick17k dataset focusing on Acne, Psoriasis, and Vitiligo is used. The Fitzpatrick Skin Type Scale is reduced from six to three levels due to image availability. VGG16 is applied to classify skin tone classes. Two additional datasets, the original and traditionally augmented datasets, are used for comparison against the StyleGAN3-augmented dataset. Results show that the StyleGAN3-augmented dataset achieves the best performance with F1-scores of 0.7193, 0.7446, and 0.7543 and accuracy scores of 0.7200, 0.7368, and 0.7419 for Acne, Psoriasis, and Vitiligo respectively. The obtained results demonstrate that utilizing the StyleGAN3-augmented dataset for skin disease classification in minority skin tone classes yields the highest performance, with F1-scores increasing by 19.25%, 19.77%, and 25.94 % for Acne, Psoriasis, and Vitiligo, respectively. These findings highlight the effectiveness of StyleGAN3 in improving model performance for underrepresented skin tones in skin disease classification. © 2024 IEEE.

关键词： Dermatology

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