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Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus 基于区块链的增强ERP事务完整性架构和PoET共识
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019416
Tehreem Aslam, A. Maqbool, M. Akhtar, Alina Mirza, Muhammad Anees Khan, Wazir Zada Khan, Shadab Alam
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引用次数: 17
Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms 可靠性-冗余分配问题的优化:进化算法综述
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020098
A. Zaka, R. Jabeen, Kanwal Iqbal Khan
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引用次数: 2
DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code DLBT:基于深度学习的从源代码生成伪代码的转换器
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019884
Walaa K. Gad, Anas Alokla, Waleed Nazih, M. Aref, A. M. Salem
: Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language translator. The DLBT is based on the transformer which is an encoder-decoder structure. There are three major components: tokenizer and embeddings, transformer, and post-processing. Each code line is tokenized to dense vector. Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network (RNN). At the post-processing step, the generated Pseudo-code is optimized. The proposed model is assessed using a real Python dataset, which contains more than 18,800 lines of a source code written in Python. The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network (RNN). The proposed DLBT records 47.32, 68. 49 accuracy and BLEU performance measures, respectively.
:当源代码是用不熟悉的语言编写时,理解源代码的内容及其正则表达式是非常困难的。伪代码在不使用语法或编程语言技术的情况下解释和描述代码的内容。然而,为每个代码指令编写伪代码是很费力的。最近,神经机器翻译被用于生成源代码的文本描述。本文提出了一种基于深度学习的变压器(DLBT)模型,用于从源代码自动生成伪代码。该模型使用基于神经机器翻译(NMT)的深度学习作为语言翻译。DLBT是基于变压器的,它是一个编码器-解码器结构。有三个主要组件:标记器和嵌入、转换器和后处理。每个代码行被标记为密集向量。然后,transformer在不需要递归神经网络(RNN)的情况下捕获源代码与匹配伪代码之间的相关性。在后处理步骤中,对生成的伪代码进行优化。所提出的模型使用真实的Python数据集进行评估,该数据集包含超过18,800行用Python编写的源代码。与其他机器翻译方法(如递归神经网络(RNN))相比,实验显示了良好的性能。拟议的DLBT记录为47.32,68。49精度和BLEU性能测量分别。
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引用次数: 5
Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model 子宫颈癌诊断模型的最优深度卷积神经网络
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020713
M. Waly, M. Sikkandar, M. Aboamer, S. Kadry, O. Thinnukool
: Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identificationand diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images. The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification. Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image. The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly. The cell images are fed into the DL based SqueezeNet model to extract deep-learned features. Finally,the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells. For experimental validation, the Herlev database is employed. The database was developed at Herlev University Hospital (Den-mark). The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.
生物医学成像是检查人体内部器官及其疾病的有效手段。巴氏涂片图像是一种重要的生物医学图像,广泛用于宫颈癌的诊断。宫颈癌是妇女死亡率上升的一个重要原因。适当的子宫颈抹片检查对于帮助宫颈癌的早期识别和诊断过程至关重要。用于癌细胞检测的计算机辅助系统需要使用深度学习(DL)方法开发。本文介绍了一种基于生物医学子宫颈抹片图像的智能深度卷积神经网络宫颈癌检测与分类(IDCNN-CDC)模型。提出的IDCNN-CDC模型包括预处理、分割、特征提取和分类四个主要过程。首先,采用高斯滤波(GF)技术对巴氏涂片图像进行去噪处理,增强数据。tallis熵技术结合蜻蜓优化(TE-DFO)算法确定图像的分割,以正确识别病变部分。细胞图像被输入到基于深度学习的SqueezeNet模型中,以提取深度学习的特征。最后,将从SqueezeNet中提取的特征应用到加权极值学习机(ELM)分类模型中,对宫颈细胞进行检测和分类。为了进行实验验证,采用了Herlev数据库。该数据库是在Herlev大学医院(丹麦-马克)开发的。实验结果表明,所提出的技术在敏感性、特异性、准确性和F-Score方面具有较高的性能。
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引用次数: 13
A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data 一种新的模糊自适应不平衡数据分类算法
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017114
Harshita Patel, D. Rajput, O. Stan, L. Miclea
Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.
不平衡数据的分类是数据挖掘和机器学习社区中一个很好的探索问题,其中一个类表示被其他类淹没。数据的不平衡分布在现实世界的数据集中是一种自然现象,因此需要仔细处理以获得重要的见解。在数据集不平衡的情况下,传统的分类器不得不牺牲其性能,从而导致误分类。本文提出了一种模糊加权最近邻法来处理这一问题。我们已经调整了“现有的算法修改解决方案”,从不平衡的数据集中学习数据分类,而不像其他流行的数据平衡方法那样操纵数据的自然分布。K近邻是一种非参数分类方法,主要用于机器学习问题。基于最近邻的模糊分类清除了实例对类的归属,改进了最近邻概念的最优权值有助于正确分类不平衡数据。该方法兼顾了数据的不平衡性,降低了传统分类器和原始分类器在应用中出现的不准确性。结果表明,该方法在不平衡学习方面优于现有的模糊近邻和加权近邻策略。
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引用次数: 7
Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion 基于模糊的多焦点图像融合混合焦点值估计
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019691
M. Aasim Qureshi, M. Asif, M. Fadzil Hassan, Ghulam Mustafa, Muhammad Khurram Ehsan, Aasim Ali, Unaza Sajid
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引用次数: 0
Convolutional Neural Network Based Intelligent Handwritten Document Recognition 基于卷积神经网络的智能手写文档识别
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.021102
Sagheer Abbas, Yousef Alhwaiti, A. Fatima, M. A. Khan, Muhammad Adnan Khan, Taher M. Ghazal, Asma Kanwal, Munir Ahmad, Nouh Sabri Elmitwally
: This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.
本文提出了一种基于卷积神经网络技术的手写文档识别系统。在当今世界,手写体文档识别因其作为视障用户辅助技术的良好表现而迅速受到研究人员的关注。该技术对数据自动录入系统也有一定的帮助。在提出的系统中,准备了一个英文手写字符图像数据集。该系统已经在大量样本数据集上进行了训练,并在用户自定义手写文档的样本图像上进行了测试。在本研究中,多次实验得到了非常有价值的识别结果。该系统将首先执行图像预处理阶段,为使用卷积神经网络进行训练准备数据。在此处理之后,使用行、词和字符分割对输入文档进行分割。该系统在字符分割过程中的准确率高达86%。然后将这些被分割的字符发送到卷积神经网络进行识别。本文提出的识别和分割技术是在给定的数据集上提供最可接受的准确结果。本文提出的方法使卷积神经网络训练时的结果准确率达到93%,验证时的准确率略有下降,为90.42%。
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引用次数: 49
Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete 基于卷积神经网络的混凝土氯离子扩散系数回归预测
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017262
Hyun Kyu Shin, Ha Young Kim, Sang Hyo Lee
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引用次数: 2
Deep Learning Approach for Analysis and Characterization of COVID-19 基于深度学习的COVID-19分析与表征方法
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019443
I. Kumar, Sultan S. Alshamrani, Abhishek Kumar, Jyoti Rawat, K. Singh, M. Rashid, A. Alghamdi
Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew's correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases. © 2021 Tech Science Press. All rights reserved.
对COVID-19等大流行疾病的早期诊断可以帮助应对严峻形势,并帮助放射科医生和其他专家更有效地管理人力资源。在最近的一次大流行中,实验室手动进行诊断,这需要实验室技术人员花费大量时间和专业知识才能得出准确的结果。此外,试剂盒的成本很高,并且需要设备齐全的实验室来进行这项测试。因此,其他的诊断手段是非常可取的。x线摄影是诊断COVID-19的现有方法之一。x线摄影观察患者的计算机断层扫描(CT)胸部图像的变化,开发了一种基于深度学习的方法来提取图形特征,用于在基于实验室的测试之前自动诊断疾病。这项工作提出了一种基于人工智能(AI)的技术,通过提取患者的视觉特征,然后在深度学习模块中使用这些特征,从给定的胸部CT图像中快速诊断COVID-19。本文提出的卷积神经网络旨在对传染性和非传染性SARS-COV2受试者进行分类。该网络使用了746张胸部扫描CT图像,其中349张属于COVID-19阳性病例,397张属于COVID-19阴性病例。我们的实验结果显示,准确率为98.4%,灵敏度为98.5%,特异性为98.3%,精密度为97.1%,f1评分为97.8%。使用分类误差、平均绝对误差(MAE)、均方根误差(RMSE)和马修相关系数(MCC)等附加参数来评估我们提出的工作。所获得的结果表明,该方法对COVID-19病例的传染性和非传染性分类具有出色的性能。©2021科技科学出版社。版权所有。
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引用次数: 4
An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors 基于集成学习的covid - 19谣言检测与跟踪方法
IF 3.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.018972
Sultan Noman Qasem, Mohammed Al-Sarem, Faisal Saeed
Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people's lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media. Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information. However, very few studies have been conducted for this purpose for the Arabic language, which has unique characteristics. Therefore, this paper proposes a comprehensive approach which includes two phases: detection and tracking. In the detection phase of the study carried out, several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset. A new detection model was used which combined two models: The Genetic Algorithm Based Support Vector Machine (that works on users' and tweets' features) and the stacking ensemble method (that works on tweets' texts). In the tracking phase, several similarity-based techniques were used to obtain the top 1% of similar tweets to a target tweet/post, which helped to find the source of the rumors. The experiments showed interesting results in terms of accuracy, precision, recall and F1-Score for rumor detection (the accuracy reached 92.63%), and showed interesting findings in the tracking phase, in terms of ROUGE L precision, recall and F1-Score for similarity techniques. © 2021 Tech Science Press. All rights reserved.
关于COVID - 19等流行病、药物和治疗、诊断方法和突发公共事件的谣言会对健康以及人们生活的政治、社会和其他方面产生有害影响,特别是在紧急情况和健康危机期间。在这种情况下,每秒钟都会有大量内容被发布到社交媒体上,因此很难发现对医疗保健行业的稳定和可持续性构成威胁的假新闻(谣言)。谣言被定义为未经证实其真实性的陈述。在新冠肺炎疫情期间,由于社交媒体上大量未经证实的信息,人们很难轻易获得最真实的新闻。在新冠肺炎相关信息中,有几种方法可以用来检测谣言和追踪谣言来源。然而,很少为此目的对具有独特特点的阿拉伯语进行研究。因此,本文提出了一种包括检测和跟踪两个阶段的综合方法。在进行的研究的检测阶段,在Arcov-19数据集上应用了几种独立和集成机器学习方法。使用了一种新的检测模型,它结合了两个模型:基于遗传算法的支持向量机(用于用户和推文的特征)和堆叠集成方法(用于推文的文本)。在跟踪阶段,使用了几种基于相似性的技术来获取与目标tweet/帖子相似的前1%的tweet,这有助于找到谣言的来源。实验在谣言检测的正确率、精密度、召回率和F1-Score方面得出了有趣的结果(正确率达到92.63%),在跟踪阶段,在相似技术的ROUGE L精密度、召回率和F1-Score方面也得出了有趣的结果。©2021科技科学出版社。版权所有。
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引用次数: 10
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Cmc-computers Materials & Continua
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