Cmc-computers Materials & Continua最新文献_第10页

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

Cmc-computers Materials & Continua 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

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020098

A. Zaka, R. Jabeen, Kanwal Iqbal Khan

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019884

Walaa K. Gad, Anas Alokla, Waleed Nazih, M. Aref, A. M. Salem

{"title":"DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code","authors":"Walaa K. Gad, Anas Alokla, Waleed Nazih, M. Aref, A. M. Salem","doi":"10.32604/cmc.2022.019884","DOIUrl":"https://doi.org/10.32604/cmc.2022.019884","url":null,"abstract":": 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.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"2016 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86287296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020713

M. Waly, M. Sikkandar, M. Aboamer, S. Kadry, O. Thinnukool

{"title":"Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model","authors":"M. Waly, M. Sikkandar, M. Aboamer, S. Kadry, O. Thinnukool","doi":"10.32604/cmc.2022.020713","DOIUrl":"https://doi.org/10.32604/cmc.2022.020713","url":null,"abstract":": 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.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"63 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86507203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017114

Harshita Patel, D. Rajput, O. Stan, L. Miclea

{"title":"A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data","authors":"Harshita Patel, D. Rajput, O. Stan, L. Miclea","doi":"10.32604/cmc.2022.017114","DOIUrl":"https://doi.org/10.32604/cmc.2022.017114","url":null,"abstract":"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.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"26 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86644718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cmc-computers Materials & Continua 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

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

Cmc-computers Materials & Continua 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

{"title":"Convolutional Neural Network Based Intelligent Handwritten Document Recognition","authors":"Sagheer Abbas, Yousef Alhwaiti, A. Fatima, M. A. Khan, Muhammad Adnan Khan, Taher M. Ghazal, Asma Kanwal, Munir Ahmad, Nouh Sabri Elmitwally","doi":"10.32604/cmc.2022.021102","DOIUrl":"https://doi.org/10.32604/cmc.2022.021102","url":null,"abstract":": 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%.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89531193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017262

Hyun Kyu Shin, Ha Young Kim, Sang Hyo Lee

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

Cmc-computers Materials & Continua 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

{"title":"Deep Learning Approach for Analysis and Characterization of COVID-19","authors":"I. Kumar, Sultan S. Alshamrani, Abhishek Kumar, Jyoti Rawat, K. Singh, M. Rashid, A. Alghamdi","doi":"10.32604/cmc.2022.019443","DOIUrl":"https://doi.org/10.32604/cmc.2022.019443","url":null,"abstract":"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.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"339 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83171140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.018972

Sultan Noman Qasem, Mohammed Al-Sarem, Faisal Saeed

{"title":"An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors","authors":"Sultan Noman Qasem, Mohammed Al-Sarem, Faisal Saeed","doi":"10.32604/cmc.2022.018972","DOIUrl":"https://doi.org/10.32604/cmc.2022.018972","url":null,"abstract":"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.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83253209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10