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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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Analyzing the Trade-Off Between Complexity Measures, Ambiguity in Insertion System and Its Application in Natural Languages 11th

Analyzing the Trade-Off Between Complexity Measures, Ambigu...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Mahendran, Anand Kannan, Kumar Hamada, Mohammed Laboratory of Theoretical Computer Science Higher School of Economics Moscow Russia School of Computer Science and Engineering Vellore Institute of Technology Vellore India Software Engineering Lab The University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9789811988240

Insertion is one of the basic operations in DNA computing. Based on this basic operation, an evolutionary computation model, the insertion system, was defined. For the evolutionary computation model defined above, varying levels of ambiguity and basic descriptional complexity measures have been defined. In this paper, we define twelve new (descriptional) complexity measures based on the integral parts of the derivation, such as axioms, strings to be inserted, and contexts used in the insertion rules. Later, we analyze the trade-off among the complexity measures and the existing ambiguity levels. Finally, we examine the application of the analyzed trade-off in natural languages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Economic and social effects

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Prediction of the impact of college students' behavior on employment rate 3

Prediction of the impact of college students' behavior on em...

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2022 3rd International Conference on computer Information and Big Data Applications, CIBDA 2022

作者： Liu, Xiang Zhang, Yikuan Department of Computer Science Software Engineering Institute of Quangzhou Guangzhou China Department of Electronic Software Engineering Institute of Quangzhou Guangzhou China

ISBN: (纸本)9783800758760

With the rapid development of artificial intelligence and the continuous improvement of prediction methods, analyzing and researching the changing trend of student employment, optimizing the direction of teaching improvement, and improving the employment rate of students have a good role. This paper mainly introduces several key factors that affect employment based on the behavior of college students, establishes a BP neural network employment rate prediction model, and predicts the employment rate of recent graduates by training and learning the employment rate of recent graduates. Comparing the training results with the real employment rate, the error is less than 2%, which shows that the employment rate prediction model based on BP neural network has practical value. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Employment

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Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

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SN computer science 2024年第2期5卷 246页

作者： Panda, Dev Sourav Dixit, Rahul Dixit, Anuja Dwaracherla, Harshitha Shukla, Anupam Bosch Digital Department Bosch Global Software Technologies Private Limited Karnataka Bangalore India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithm LSTM model Reproduction number SEIR-type compartmental model Social media Transmission dynamics

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data 24

GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labe...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhao, Haochen Meng, Hui Yang, Deqian Xie, Xiaozheng Wu, Xiaoze Li, Qingfeng Niu, Jianwei School of Computer Science and Engineering Beihang University Beijing China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China Institute of Software Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9798400706868

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ *** state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled *** address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs.A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same *** this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated ***, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-the-art performance. © 2024 ACM.

关键词： Self-supervised learning

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community 63

On final opinions of the Friedkin-Johnsen model over random ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Lingfei Xing, Yu Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness. © 2024 IEEE.

关键词： Graph theory

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Deep Ear Biometrics for Gender Classification 4th

Deep Ear Biometrics for Gender Classification

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB Haldia India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani Campus 333031 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9789819927098

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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