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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani department of computer science University of KarachiKarachi75270Pakistan department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia department of computer science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Image enhancement with intensity transformation on embedding space

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 101-115页

作者： Hanul Kim Yeji Jeon Yeong Jun Koh department of applied Artificial Intelligence Seoul National University of Science and TechnologySeoulSouth Korea department of computer science and Engineering Chungnam National UniversityDaejeonSouth Korea

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained ***,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality *** order to address this limitation,a simple yet effective approach for image enhancement is *** proposed algorithm based on the channel-wise intensity transformation is ***,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to *** this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding ***,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced *** experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space ***,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch ***,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

关键词： computer vision deep learning image enhancement image processing

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Spatial geometric constraints histogram descriptors based on curvature mesh graph for 3D pollen particles recognition

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Chinese Physics B 2014年第6期23卷 123-130页

作者：谢永华徐赵飞 Hans Burkhardt School of computer and Software Nanjing University of Information Science and Technology department of computer science Freiburg University

This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.

关键词： pollen recognition curvature mesh graph spatial geometric constraints bidirectional histogram

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Controllable optical bistability of Bose-Einstein condensate in an optical cavity with a Kerr medium

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Chinese Physics B 2012年第9期21卷 229-233页

作者：郑强栗生长张小平游泰杰傅立斌 School of Mathematics and computer science Guizhou Normal University Institute of applied Physics and Computational Mathematics department of Engineering Physics Tsinghua University

We study the optical bistability for a Bose-Einstein condensate of atoms in a driven optical cavity with a Kerr medium. We find that both the threshold point of optical bistability transition and the width of optical bistability hysteresis can be controlled by appropriately adjusting the Kerr interaction between the photons. In particular, we show that the optical bistability will disappear when the Kerr interaction exceeds a critical value.

关键词： Bose-Einstein condensate optical bistability Kerr interaction

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Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

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computers, Materials & Continua 2022年第7期72卷 797-814页

作者： Ayman M.Abdalla Mohammad Abu Awad Omar AlZoubi La’aly A.Al-Samrraie department of computer science Al-Zaytoonah University of JordanAmman11733Jordan department of computer science Jordan University of Science and TechnologyIrbid22110Jordan department of Water and Environmental Engineering Al-Huson University CollegeAl-Balqa Applied UniversityIrbidJordan

The enlarged veins in the pampiniform venous plexus,known as varicocele disease,are typically identified using ultrasound *** diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male *** proposed system is designed to determine whether a patient is *** is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other ***,the experimental results of this study focused on images taken in the supine position of the left testicles of *** are two possible vein structures in each image:a cross-section(circular)and a tube(non-circular)*** proposed system identifies dilated(varicocele)veins of these structures in ultrasound images in three stages:preprocessing,processing,and detection and *** three stages are applied in three different color modes:Grayscale,Red-Green-Blue(RGB),and Hue,Saturation,and Value(HSV).In the preprocessing stage,the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold ***,the processing stage employs different filters to perform image ***,edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins *** implementation results showed the proposed system is faster andmore effective than the previous work.

关键词： Varicocele color mode HSV median filter segmentation canny

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Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

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computer Modeling in Engineering & sciences 2024年第3期138卷 2471-2495页

作者： Wimonmas Bamrungsetthapong Pramote Charongrattanasakul Division of applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull *** control chart developed supports the examination of the mean lifespan variation for a particular product in the process of *** control limit levels are used:the warning control limit,inner control limit,and outer control ***,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control *** control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control ***,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

关键词： Modified DS np chart generalizedmultiple dependent state sampling time truncated life test Weibull distribution average run length average sample size

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Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

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computers, Materials & Continua 2021年第9期68卷 3465-3481页

作者： Ghulfam Zahra Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Fayez Eid Alazemi department of computer science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan department of computer science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of applied computer science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia department of computer science and Information Systems College of Business StudiesPAAET12062Kuwait

:In recent years,video surveillance application played a significant role in our daily *** taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the *** reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and *** remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the ***,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision *** existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric *** research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze *** proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct *** generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free *** are conducted on FRIDA dataset and haze *** assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance *** results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so *** results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.

关键词： Video surveillance degraded images image restoration transmission map visibility enhancement

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On two Hall algebra approaches to odd periodic triangulated categories

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science China Mathematics 2018年第2期61卷 317-324页

作者： Jie Sheng Xueqing Chen Fan Xu department of applied Mathematics College of Science China Agricultural University department of Mathematical and computer sciences University of Wisconsin-Whitewater department of Mathematical sciences Tsinghua University

Given an odd-periodic algebraic triangulated category, we compare Bridgeland's Hall algebra in the sense of Bridgeland(2013) and Gorsky(2014), and the derived Hall algebra in the sense of Ten(2006), Xiao and Xu(2008) and Xu and Chen(2013), and show that the former one is the twisted form of the tensor product of the latter one and a suitable group algebra.

关键词： Hall algebra derived Hall algebra odd-periodic triangulated category

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Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment

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Tsinghua science and Technology 2013年第1期18卷 62-67页

作者： Jing Wang Guangda Su Ying Xiong Jiansheng Chen Yan Shang Jiongxin Liu Xiaolong Ren the department of Electronic Engineering Tsinghua University the department of Electronic Engineering Tsinghua University the department of Engineering and applied sciences Harvard University the department of computer science Columbia University

Sparse Representation based Classification （SRC） has emerged as a new paradigm for solving recognition problems. This paper presents a constraint sampling feature extraction method that improves the SRC recognition rate. The method combines texture and shape features to significantly improve the recognition rate. Tests show that the combined constraint sampling and facial alignment achieves very high recognition accuracy on both the AR face database （99.52%） and the CAS-PEAL face database （99.54%）.

关键词： classification face recognition feature extraction face alignment

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Traffic Sign Recognition Using an Attentive Context Region-Based Detection Framework

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Chinese Journal of Electronics 2021年第6期30卷 1080-1086页

作者： LIU Zhigang DU Juan TIAN Feng WEN Jiazheng School of computer and Information Technology Northeast Petroleum University Postdoctoral Workstation on applied Technology Research Institute Northeast Petroleum University department of computer science for Artificial Intelligence University of Nottingham Ningbo

Accurate small traffic sign recognition is more important for the safety of intelligent transportation systems. A recognition framework named attentive context region-based detection framework(AC-RDF) is proposed in this paper. We construct the attentive context feature for the recognition of small traffic signs,which combines the target information and the contextual information by the concatenation operation following a pointwise convolutional layer. The proposed attentive context feature exploits the surrounding information for a given object proposal. Next, we propose a novel attentive loss function to replace the original crossentropy function. It distinguishes hard negative samples from easy positive ones in the total loss, allows the proposed framework to obtain enough training, and further improve the recognition accuracy. The proposed method is evaluated on the challenging Tsinghua-Tencent 100 K dataset. The experimental results indicate that the attentive context region-based detection framework is superior at detecting small traffic signs and achieves stateof-the-art performance compared with other methods.

关键词： attentive context region-based detection framework intelligent transportation systems convolutional neural nets attentive context feature AC-RDF framework attentive loss function traffic signs learning (artificial intelligence) Tsinghua-Tencent 100K dataset object recognition feature extraction traffic sign recognition object detection entropy recognition framework

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