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

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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

Adaptive Segmentation for Unconstrained Iris Recognition

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Computers, Materials & Continua 2024年第2期78卷 1591-1609页

作者： Mustafa AlRifaee Sally Almanasra Adnan Hnaif Ahmad Althunibat Mohammad Abdallah Thamer Alrawashdeh faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia faculty of Science and information technology Al-Zaytoonah University of JordanAmman11733Jordan

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture *** these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction ***,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained ***,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera *** image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous *** iris images are accordingly classified into seven categories based on their global RGB *** the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ***,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris *** experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

关键词： Image recognition color segmentation image processing localization

Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

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Computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih Maiwan Bahjat Abdulrazaq Department of information technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

Effects of diamagnetic drift on nonlinear interaction between multi-helicity neoclassical tearing modes

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Chinese Physics B 2024年第6期33卷 452-458页

作者：王海源姜帅刘桐魏来栾其斌王正汹 Key Laboratory of Materials Modification by Laser Ionand Electron Beams of the Ministry of EducationSchool of PhysicsDalian University of TechnologyDalian 116024China faculty of Electronic information and Electrical Engineering Dalian University of TechnologyDalian 116024China

A numerical study of the diamagnetic drift effect on the nonlinear interaction between multi-helicity neoclassical tearing modes(NTMs) is carried out using a set of four-field equations including two-fluid *** results show that,in contrast to the single-fluid case,5/3 NTM cannot be completely suppressed by 3/2 NTM with diamagnetic drift *** modes exhibit oscillation and coexist in the saturated *** better understand the effect of the diamagnetic drift flow on multiple-helicity NTMs,the influence of typical relevant parameters is *** is found that the average saturated magnetic island width increases with increasing bootstrap current fraction f_(b) but decreases with the ion skin depth δ.In addition,as the ratio of parallel to perpendicular transport coefficients χ_(‖)/χ_(⊥) increases,the average saturated magnetic island widths of the 3/2 and 5/3 NTMs *** underlying mechanisms behind these observations are discussed in detail.

关键词： tokamak two-fluid neoclassical tearing modes multi-helicity

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Expression Complementary Disentanglement Network for Facial Expression Recognition

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Chinese Journal of Electronics 2024年第3期33卷 742-752页

作者： Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU College of Computer Science and technology Nanjing University of Aeronautics and Astronautics College of Computer and Software Nanjing University of Information Science and Technology School of information and Control Nanjing University of Information Science and Technology

Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression *** methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial *** to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input *** solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during *** from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching *** designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts *** geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,*** combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED *** results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关键词： Geometry Image recognition Annotations Face recognition Task analysis Image reconstruction Facial features

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

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Computers, Materials & Continua 2024年第1期78卷 1115-1133页

作者： Amira M.Idrees Abdul Lateef Marzouq Al-Solami faculty of Computers and information technology Future University in EgyptCairo11835Egypt Alkamil College of Science and Arts University of JeddahJeddah21959Saudi Arabia

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual *** modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the *** research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice *** leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind *** modular design ensures flexibility,scalability,and ease of integration with existing assistive ***,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and ***-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification ***,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate ***,this research paves the way for the development of more advanced and robust systems for assisting visually impaired *** leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual *** work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

关键词： Social networks text analytics emoji prediction features extraction information retrieval

Quantum key distribution over a mimicked dynamic-scattering channel

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Science China(information Sciences) 2024年第4期67卷 323-330页

作者： Qi-Hang LU Fang-Xiang WANG Wei CHEN Hai-Yang FU Yin-Jie LU Shuang WANG De-Yong HE Zhen-Qiang YIN Guang-Can GUO Zheng-Fu HAN CAS Key Laboratory of Quantum information University of Science and Technology of China CAS Center for Excellence in Quantum information and Quantum Physics University of Science and Technology of China Hefei National Laboratory University of Science and Technology of China

Free-space quantum key distribution(QKD) plays an important role in the global quantum network. However, free space channels suffer from the atmospheric turbulence and scattering effects of haze,fog, and dust, which significantly weaken the performance of QKD or even block the secure quantum ***, we prove the performance of QKD over a dynamic scattering channel can be enhanced significantly using a fast wavefront shaping technique. The system achieves on average 10-dB enhancement of quantum transmission efficiency and establishes a secure quantum link for QKD. Our work demonstrates the feasibility of QKD through free-space dynamic scattering channels and enhances the deployment capability of complex-channel QKD system.

关键词： quantum key distribution free space channel channel turbulence channel scattering wavefront shaping

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Effect of Deposition Temperature on Optical Properties of Porous Amorphous SiC Film

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Journal of Wuhan University of technology(Materials Science) 2024年第4期39卷 839-844页

作者：徐吉祥 TAO Weijie LIU Canhui XU Haoyao LI Lixuan 贺振华 School of information Engineering Wuhan University of TechnologyWuhan 430070China

To study the effect of different deposition temperatures on the optical properties of porous SiC films,single crystal Si was used as the substrate,a layer of anodic aluminum oxide(AAO)film was transferred on the Si substrate by chemical method,and then a layer of SiC was deposited on anodic aluminum oxide(AAO)template to prepare porous fluorescent SiC film by magnetron *** deposition temperature was ranged from 373 to 873 *** thickness of the porous SiC film coated on the AAO surface was around 283 *** is found that the porous SiC with the deposition temperature of 873 K has the strongest photoluminescence(PL)intensity excited by 375 nm *** time-resolved PL spectra prove that the PL is mainly from intrinsic light emitting of *** the optimized process,porous amorphous SiC film may have potential applications in the field of warm white LEDs.

关键词： porous SiC film fluorescent SiC anodic aluminum oxide magnetron sputtering time-resolved spectrum

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Linear shallow neural network to accelerate transmitter dispersion eye closure quaternary(TDECQ) assessment

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Science China(information Sciences) 2024年第4期67卷 337-338页

作者： Junjiang XIANG Zejun CHEN Yijun CHENG Hailin YANG Xuancheng HUO Meng XIANG Gai ZHOU Yuwen QIN Songnian FU Institute of Advanced Photonics technology School of Information EngineeringGuangdong University of Technology School of Optical and Electronic information Huazhong University of Science and Technology Key Laboratory of Photonic technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of Technology Guangdong Provincial Key Laboratory of information Photonics technology Guangdong University of Technology

To support the ever-emerging capacity requirement of datacenter interconnection(DCI) applications, transmitter dispersion eye closure quaternary(TDECQ) has been proposed by IEEE802.3 standard [1], which is specifically employed for the four-level pulse amplitude modulation(PAM-4) signals. However, the traditional scheme of TDECQ assessment needs several iterative operations,

关键词： IEEE Standards

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