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Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

computers, Materials & Continua 2021年第6期67卷 3329-3348页

作者： Emad-ul-Haq Qazi Muhammad Hussain Hatim A.AboAlsamh Visual Computing Lab Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The detection of alcoholism is of great importance due to its effects on individuals and *** alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging ***’current designs are based on conventional,hand-engineered methods and restricted *** by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD *** order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable *** the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or *** validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection *** order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and *** results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the *** proposed AADS will be useful in medical diagnosis research and health care systems.

关键词： Electroencephalogram convolutional neural network deep learning alcoholism

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Perceptual Evaluation Based on WOA-BP Neural Network 2

Perceptual Evaluation Based on WOA-BP Neural Network

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2nd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Juanni, Jiao Yong, Zheng Wei, Li College of Computer Science Sichuan Normal University Chengdu China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu China

ISBN: (纸本)9798350371451

In modern society, various patterns play an indispensable role. With the help of computer technology, the efficiency of designers in the field of pattern design has been significantly improved. However, in the process of pattern design, we often only rely on the subjective experience of designers, while ignoring the personal preferences of consumers. In order to explore the relationship between different pattern designs and consumers' perceptual evaluation, the Kansei engineering theory is first employed to capture consumers' perceptual evaluation of the patterns. Subsequently, various pattern designs and consumers' perceptual evaluation were utilized as inputs and outputs for the WOA-BP neural network, thereby constructing an intelligent model capable of predicting consumers' perceptual evaluation of different pattern designs. The experimental results demonstrate that the model has a highly accurate prediction level. With this model, designers can quickly adjust the pattern design to better meet the aesthetic needs of today's consumers. © 2023 IEEE.

关键词： Professional aspects

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Enhanced Multi-Style Transfer Method based on VGG Network 2

Enhanced Multi-Style Transfer Method based on VGG Network

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2nd International Conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Zheng, Yong Jiao, Juanni Ye, Fange Li, Wei Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan China

ISBN: (纸本)9798350331448

In the domain of conventional image style transfer, remarkable progress has been achieved;nonetheless, certain issues persist. A large grayscale image with only one channel of brightness information lacks diverse color and texture features. This limits the variety in style transformation, resulting in unsatisfactory diversity of effects. To overcome this challenge, the present study introduces an enhanced multi-style transfer approach founded upon the VGG network. To achieve a greater variety of style transfers for single-channel grayscale images. This is accomplished by introducing a dual VGG network architecture coupled with the incorporation of the Gram matrix concept. Additionally, a multi-style fusion module is introduced to merge various style transfers for single-channel grayscale images. The experimental outcomes unequivocally demonstrate that the proposed approach effectively accomplishes manifold style transfers within single-channel grayscale images. Furthermore, apart from retaining the inherent content attributes, it bestows images with fresh visual allure. In comparison to conventional methods, the approach showcased in this study achieves significant advancements in both the quality and diversity of style transfer. Consequently, this innovative approach not only expands the scope of image style transfer applications but also paves the way for a novel potential in the realm of style transformation for single-channel grayscale images. © 2023 IEEE.

关键词： Network architecture

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Intelligent Web Spam Detection in the Consumer Internet of Things

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1260-1273页

作者： Wang, Rong Zhuang, Xu Zhu, Xiaogang Kashif Bashir, Ali Al Dabel, Maryam M. Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China OPPO Electronics Corporation Department of Software Engineering Chengdu611743 China Nanchang University School of Public Policy and Administration Nanchang330031 China Jiangxi Institute of Industrial Technology for Internet of Things Yingtan335000 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6GB United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut03797751 Lebanon University of Hafr Al Batin College of Computer Science and Engineering Department of Computer Science and Engineering Hafar Al-Batin39524 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

The Consumer Internet of Things (CIoT) integrates the advantage of Internet of Things (IoT) technologies to provide convenience in consumers’ daily lives. With the rapid development of the CIoT, data collected from consumer smart devices has increased exponentially. In the CIoT, Web pages, as Internet information carriers, offer spammers opportunities to conduct security attacks, which could harm the CIoT systems. Inspired by this challenge, this paper introduces an intelligent feature extraction method, page2vec, and a new classification algorithm, RFiRF, to detect Web spam in the CIoT. page2vec is based on a score propagation model, which calculates each page’s goodness and badness scores through the links of a Web graph. It is observed that different scoring functions can produce different Web page features. Based on this observation, 20 to 30 scoring functions are designed and incorporated into page2vec. This way, page2vec can automatically extract Web page features from a Web graph. Unfortunately, page2vec also brings a high dimensionality problem when constructing binary classifiers for spam detection. To address this problem, a new classification algorithm, called Random Forest in Random Forest (RFiRF), is proposed. RFiRF replaces a random forest’s meta-classifier (decision tree) with a random forest. It divides the sparse data space into dense sub-spaces by randomly sampling training instances and features. Experiments on two benchmark datasets show that 1) page2vec features are much more predictive than the raw link-based and node2vec features, and 2) RFiRF outperforms some classification algorithms in most cases when facing the high dimensionality problem. We hope this paper can give peers valuable insights into the CIoT security. © 1975-2011 IEEE.

关键词： Feature extraction

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Real-Time 3D Object Detection, Recognition and Presentation Using a Mobile Device for Assistive Navigation

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SN computer science 2023年第5期4卷 1-18页

作者： Chen, Jin Zhu, Zhigang Visual Computing Laboratory Computer Science Department The City College-CUNY New York 10031 NY United States Nearabl Inc. New York 10023 NY United States PhD Program in Computer Science The Graduate Center-CUNY New York 10016 NY United States

This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, mask RCNN) based on the requested task and requirements of the run time and details for the 3D detection result. It can run on a cloud server or mobile application. The object tracking and update module minimizes the computational power for long-term environment scanning by converting 2D tracking results into 3D results. The voice and AR-enhanced interface integrates ARKit and SiriKit to provide voice interaction and AR visualization to improve information delivery for different user groups. The system can be integrated with existing applications, especially assistive navigation, to increase travel safety for people who are blind or have low vision and improve social interaction for individuals with autism spectrum disorder. In addition, it can potentially be used for 3D reconstruction of the environment for other applications. Our preliminary test results for the object detection evaluation and real-time system performance are provided to validate the proposed system. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 3D object detection ARKit Assistive technology Blind or low vision Voice assistance

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Few-Shot Classification Based on Feature Enhancement Network

Few-Shot Classification Based on Feature Enhancement Network

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shasha Hu Han Su Ruixuan Gao College of Computer Science Sichuan Normal University Chengdu China Visual Computing and visual reality Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Few-shot image classification stands as a pivotal task within the realm of computer vision. However, obtaining accurate class prototypes from limited annotated samples is a challenging problem. In recent years, many methods based on prototype networks have shown excellent performance. Nevertheless, existing methods overlook the discriminative semantic information lost due to sample scarcity and the hidden category information in the query set, failing to address the issue of unreliable prototypes generated from limited annotated samples. In this paper, we propose a feature enhancement network for few-shot classification. To improve the accuracy and robustness of few-shot classification models, we first enhance the support set through learning a weight matrix and then align the enhanced support set prototypes with textual semantics. To avoid being influenced by introduced prior noise, we fuse between semantically aligned prototypes and mean prototypes and ultimately utilize query prototypes for dynamic updating to obtain more accurate class prototypes. Extensive experiments demonstrate that our method achieves competitive performance on miniImageNet and tieredImageNet datasets. Furthermore, it exhibits excellent results in cross-domain few-shot classification.

关键词： computer vision Accuracy Fuses Semantics Noise Prototypes Robustness Few shot learning Cybernetics Image classification

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