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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Chuangchuang Chen, Xiaobing Lin, Jianchu Guo, Shuxin Wang, Rongrong Gu, Yinxi Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model. © 2023 IEEE.

关键词： Mesh generation

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Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

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Frontiers of computer Science 2022年第2期16卷 110-122页

作者： Awais KHAN Aun IRTAZA Ali JAVED Tahira NAZIR Hafiz MALIK Khalid Mahmood MALIK Muhammad Ammar KHAN Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Electrical and Computer Engineering University of MichiganDearbornMI 48128USA Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan Department of Computer Science and Engineering Oakland UniversityMI 48309USA

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information *** defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera ***,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned *** paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur *** argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred *** fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the ***,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy *** results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur *** and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

关键词： defocus blur detection local directional mean patterns image matting sharpness metrics blur segmentation

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Author profiling from Romanized Urdu text using transfer learning models

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Neural Computing and Applications 2025年第6期37卷 4455-4470页

作者： Ali, Abid khan, Muhammad Sohail Khan, Muhammad Amin Khan, Sajid Ullah Khan, Faheem Department of Computer Software Engineering University of Engineering & Technology Mardan KPK Mardan23200 Pakistan Islamabad H-11/4 Islamabad44000 Pakistan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering Gachon University Seongnam-Si Korea Republic of

This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBERT, Distil BERT, Distil Roberta, ELECTRA, and XLNet. Through meticulous evaluation using metrics such as the Matthews Correlation Coefficient, Accuracy, Precision, Recall, and F1 Score, the study examined the efficacy of these models. The curated dataset was divided for gender and age tasks, resulting in robust gender prediction with the XLNet model and age prediction with the BERT model. Notably, the XLNet model achieved the highest MCC (0.7946), Accuracy (0.8957), Precision (0.8992), Recall (0.8957), and F1 Score (0.8958) values in gender classification, while the BERT model excelled in age prediction with an MCC of (0.7338), Accuracy of (0.8220), Precision of (0.8324), Recall of (0.8220), and F1 Score of (0.8243). Visualized outcomes provide valuable insights into the model’s performance nuances, paving the way for their practical implementation. This research offers novel contributions to author profiling tasks, bridging the gap between theory and real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Review on Image Processing for MRI-Focused Brain Tumor Identification Using Machine Learning 7

Review on Image Processing for MRI-Focused Brain Tumor Ident...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Agha, Dur-E-Shawar Qazi, Farheen Rasheed, Rohail Ali Khan, M. Sadiq Naseem, Muhammad Mubeen, Tauseef Sir Syed University of Engineering & Technology Software Engineering Department Karachi Pakistan Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brain tumor diagnosis is the most important task for early therapy, and automatic visual classification might be extremely important. It is essential to classify brain cancers in order to assess the tumor's and determine the best course of treatment for each class. To find brain tumors, numerous imaging modalities are used. However, MRI is frequently utilized since it produces superior images and uses no ionizing radiation. This research aims to provide a review of different Machine Learning (ML) techniques for detection and classification of brain tumor. Also, different feature extraction techniques are analyzed and their benefits are discussed. This review is conducted by different research papers which helps new researchers to start immediately a literature review and knowing different machine learning techniques their advantages and disadvantages to classify brain tumor through MRI. Also, they know the accuracy of each algorithm which leads them to directly target the problem not implementing the existing techniques. © 2024 IEEE.

关键词： Lung cancer

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Binary Program Vulnerability Mining Based on Neural Network

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computers, Materials & Continua 2024年第2期78卷 1861-1879页

作者： Zhenhui Li Shuangping Xing Lin Yu Huiping Li Fan Zhou Guangqiang Yin Xikai Tang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical and Computer Engineering University ofWaterlooWaterlooN2L 3G1Canada

software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates

关键词： Vulnerability mining de-obfuscation neural network graph embedding network symbolic execution

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LSRN-AED: lightweight super-resolution network based on asymmetric encoder–decoder

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Soft Computing 2024年第13-14期28卷 8513-8525页

作者： Huang, Shuying Li, Wei Yang, Yong Wan, Weiguo Lai, Houzeng School of Software Tiangong University Tianjin300387 China School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang330032 China School of Computer Science and Technology Tiangong University Tianjin300387 China

Due to limited memory and computing resources, the application of deep neural networks on embedded and mobile devices is still a great challenge. To tackle this problem, this paper proposes a lightweight super-resolution network based on asymmetric encoder–decoder (LSRN-AED), which achieves better performance while reducing model parameters and computation. On the basis of rethinking the roles of encoder and decoder, an asymmetric encoder–decoder (AED) composed of complex encoders and simple decoders is designed to achieve feature extraction and reconstruction. Here, the decoder only adopts one inverted residual block, which can reduce the computational cost of the model and the redundancy of mapping features. For the encoder, inspired by the Transformer structure, an epiphany encoder is designed to realize the feature extraction and representation. In the encoder, a multi-way epiphany attention module (MEAM) is constructed, in which inverted residual blocks are used to replace traditional residual blocks to extract features and reduce model complexity. To realize the selection and enhancement of spatial features, an epiphany attention block (EAB) is designed by exploiting depth-wise convolutions which can learn the significant spatial information of the feature maps. Experimental results demonstrate that the proposed LSRN-AED can achieve better performance at lower parameter cost and outperform some existing state-of-the-art lightweight models. For example, compared to the advanced SMSR method, the proposed LSRN-AED has better evaluation metrics while reducing the number of parameters by 45%, 44%, and 44%, and FLOPs by 44%, 42%, and 41% on the × 2/3/4 SR tasks, respectively. The code will be published on GitHub after our paper is accepted for publication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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ForkXplorer:an approach of fork summary generation

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Frontiers of computer Science 2022年第2期16卷 1-15页

作者： Zhang ZHANG Xinjun MAO Chao ZHANG Yao LU Key Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Pull-based development has become an important paradigm for distributed software *** this model,each developer independently works on a copied repository(i.e.,a fork)from the central *** is essential for developers to maintain awareness of the state of other forks to improve collaboration *** this paper,we propose a method to automatically generate a summary of a *** first use the random forest method to generate the label of a fork,i.e.,feature implementation or a bug *** on the information of the fork-related commits,we then use the TextRank algorithm to generate detailed activity information of the ***,we apply a set of rules to integrate all related information to construct a complete fork *** validate the effectiveness of our method,we conduct 30 groups of manual experiment and 77 groups of case studies on *** propose Fea_(avg)to evaluate the performance of Fea_(avg)the generated fork summary,considering the content accuracy,content integrity,sentence fluency,and label extraction *** results show that the average of of the fork summary generated by this method is *** than 63%of project maintainers and the contributors believe that the fork summary can improve development efficiency.

关键词： open source software pull-based development fork summary distributed cooperative development

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Research On Fatigue Driving Detection Based On Deep Learning

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engineering Letters 2025年第2期33卷 348-356页

作者： Zhou, Huinan Dai, Hong University of Science and Technology Liaoning Liaoning AnshanCO 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning AnshanCO 114051 China

Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usually use a two-stage detection method, which has the problems of low efficiency and poor accuracy. To solve these problems, this paper designs a single-stage fatigue driving detection model based on separable convolution Road Safety Net (RSNet), which extracts the features in the driver's image or video and classifies these features to determine whether the driver is in an abnormal driving state. RSNet borrows the structure of Swin Transformer and adopts a hierarchical design, which makes the model better able to extract features and classify. Each level is composed of a GDS Layer and multiple RS Blocks. To solve the problem of low efficiency of traditional methods, we use separable convolution in RS Block to replace the traditional convolutional neural network, which reduces the complexity of the model and effectively improves the computational efficiency of the model. Experimental results show that the Top-1 and Top-5 Accuracy of RSNet on the HMDB51 dataset reaches 85 % and 97.35 %, respectively, and the delay is 5.97 ms. Compared with ResNet, EfficientNet, and Swin Transformer, RSNet has higher accuracy and efficiency. In addition, this paper takes the YOLACT algorithm as an example, selects different backbone networks for object detection, and compares the indicators of mean Average Precision (mAP) and Frames Per Second (FPS). Experiments show that RSNet performs better when used as the backbone network for object detection, with mAP reaching 32.4 % and FPS of 39.6 ms. It indicates that RSNet performs well in action recognition tasks. © 2025, International Association of Engineers. All rights reserved.

关键词： Convolutional neural networks

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A Ginkgo Detection Algorithm in Complex Environments Based on Improved YOLOv7 9

A Ginkgo Detection Algorithm in Complex Environments Based o...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Ma, Chi Guo, Qiang School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan China

ISBN: (纸本)9798350376548

Detections of Ginkgoes are prerequisites for later counting and harvesting. Due to the uneven distribution of samples, the detection speed and accuracy of existing algorithms cannot adapt to the impact of complex environments. Therefore, an enhanced model YOLOv7-DC based on YOLOv7 is proposed in this paper, which redesigns the detection network and introduces a new feature fusion method. DCNv2 is embedded in the efficient layer aggregation network (ELAN), while PConv is utilized instead of conventional convolution to reduce the parameter impact of DCNv2. Moreover, the attention mechanism CBAM is introduced during training to enhance spatial and channel information, and the ConvMixer architecture is employed to capture spatial and channel relationships within the features, which are transmitted to the detection head through attention mechanism, improving the model's detection accuracy for each specific classification sample. Experimental results show that our YOLOv7-DC achieves both excellent detection speed and recognition rate in various classification tasks. The improved model's average detection accuracy is increased by 6.2% compared to previous algorithms, and the model parameters are reduced by 13%. It is proved that YOLOv7-DC is more suitable for scenarios with imbalanced samples and complex environments. © 2024 IEEE.

关键词： Classification (of information)

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