Cmc-computers Materials & Continua最新文献_第5页

Feature Selection for Cluster Analysis in Spectroscopy 光谱中聚类分析的特征选择

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.022414

Simon Crase, Benjamin Hall, Suresh N. Thennadil

{"title":"Feature Selection for Cluster Analysis in Spectroscopy","authors":"Simon Crase, Benjamin Hall, Suresh N. Thennadil","doi":"10.32604/cmc.2022.022414","DOIUrl":"https://doi.org/10.32604/cmc.2022.022414","url":null,"abstract":": Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy, namely, high dimensionality and small sample size. In order to improve cluster analysis outcomes, feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality. However, for cluster analysis, this must be done in an unsupervised manner without the benefit of data labels. This paper presents a novel feature selection approach for cluster analysis, utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster. Two versions are presented and evaluated: The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality. These new techniques, along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices. Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated. However, it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected, with significant instability observed for most techniques at low numbers of features. It was identified that the genetic algorithm wrapper technique avoided this instability, performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"27 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85286196","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: 0

Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework 基于多视角基准框架的可持续安全评估

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.024903

Ahmed Saeed Alfakeeh, Abdulmohsen Almalawi, Fawaz Jaber Alsolami, Yoosef B. Abushark, Asif Irshad Khan, Adel Aboud S. Bahaddad, Md. Mottahir Alam, A. Agrawal, Rajeev Kumar, Raees Ahmad Khan

引用次数: 0

Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models 基于单图像超分辨率和CNN模型的COVID-19自动检测

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.018547

W. El-shafai, Anas M. Ali, El-sayed M. El-Rabaie, Naglaa. F. Soliman, Abeer D. Algarni, Fathi E. Abd El-Samie

{"title":"Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models","authors":"W. El-shafai, Anas M. Ali, El-sayed M. El-Rabaie, Naglaa. F. Soliman, Abeer D. Algarni, Fathi E. Abd El-Samie","doi":"10.32604/cmc.2022.018547","DOIUrl":"https://doi.org/10.32604/cmc.2022.018547","url":null,"abstract":"In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification. However, because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19, transfer learning is not usually a robust solution. Single-Image Super-Resolution (SISR) can facilitate learning to enhance computer vision functions, apart from enhancing perceptual image consistency. Consequently, it helps in showing the main features of images. Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis, this paper introduces a hybrid CNN model, namely SIGTra, to generate super-resolution versions of X-ray and CT images. It depends on a Generative Adversarial Network (GAN) for the super-resolution reconstruction problem. Besides, Transfer learning with CNN (TCNN) is adopted for the classification of images. Three different categories of chest X-ray and CT images can be classified with the proposed model. A comparison study is presented between the proposed SIGTra model and the other related CNN models for COVID-19 detection in terms of precision, sensitivity, and accuracy. © 2021 Tech Science Press. All rights reserved.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"24 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84476495","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

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis 面向方面级情感分析的自动标注技术

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020544

M. Aasim Qureshi, M. Asif, M. Fadzil Hassan, Ghulam Mustafa, Muhammad Khurram Ehsan, Aasim Ali, Unaza Sajid

{"title":"A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis","authors":"M. Aasim Qureshi, M. Asif, M. Fadzil Hassan, Ghulam Mustafa, Muhammad Khurram Ehsan, Aasim Ali, Unaza Sajid","doi":"10.32604/cmc.2022.020544","DOIUrl":"https://doi.org/10.32604/cmc.2022.020544","url":null,"abstract":": In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"14 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84527850","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: 8

Decoding of Factorial Experimental Design Models Implemented in Production Process 生产过程中析因实验设计模型的解码

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.021642

Adham Mohammed Alnadish, Mohamad Yusri Aman, Herda Yati Binti Katman, Mohd Rasdan Ibrahim

引用次数: 0

Deep Learning Based Modeling of Groundwater Storage Change 基于深度学习的地下水储量变化建模

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020495

Mohd anul haq, Abdul Khadar Jilani, P. Prabu

{"title":"Deep Learning Based Modeling of Groundwater Storage Change","authors":"Mohd anul haq, Abdul Khadar Jilani, P. Prabu","doi":"10.32604/cmc.2022.020495","DOIUrl":"https://doi.org/10.32604/cmc.2022.020495","url":null,"abstract":": The understanding of water resource changes and a proper projec-tion of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003– 2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003–2020 with a rate ranging from − 5.88 ± 1.2 mm/year to − 14.12 ± 1.2 mm/year and − 3.5 ± 1.5 to − 10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from − 7.78 ± 1.2 to − 15.6 ± 1.2 for TWSC and − 4.97 ± 1.5 to − 12.21 ± 1.5 for GWSC from 2020–2025. An interesting observation was a minor increase in rainfall during the study period for three basins.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"19 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82135235","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: 52

Dynamic Routing Optimization Algorithm for Software Defined Networking 软件定义网络的动态路由优化算法

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017787

Nancy Abbas El-Hefnawy, O. Abdel Raouf, Heba Askr

引用次数: 3

TinyML-Based Fall Detection for Connected Personal Mobility Vehicles 基于tinyml的联网个人移动车辆跌倒检测

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.022610

R. Sanchez-Iborra, Luis Bernal-Escobedo, J. Santa, A. Skarmeta

{"title":"TinyML-Based Fall Detection for Connected Personal Mobility Vehicles","authors":"R. Sanchez-Iborra, Luis Bernal-Escobedo, J. Santa, A. Skarmeta","doi":"10.32604/cmc.2022.022610","DOIUrl":"https://doi.org/10.32604/cmc.2022.022610","url":null,"abstract":"This is licensed a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract: A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful ML algorithms in constrained units. We have generated and publicly released a large dataset, including real riding measurements and realistically simulated falling events, which has been employed to produce different TinyML models. The attained results show the good operation of the system to detect falls efficiently using embedded OBUs. The considered algorithms have been successfully tested on mass-market low-power units, implying reduced energy consumption, flash footprints and running times, enabling new possibilities for this kind of vehicles.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"43 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79665499","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: 2

HARTIV: Human Activity Recognition Using Temporal Information in Videos HARTIV:利用视频中的时间信息进行人类活动识别

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.020655

D. Deotale, M. Verma, P. Suresh, Sunil Kumar Jangir, Manjit Kaur, Sahar Ahmed Idris, H. Alshazly

引用次数: 6

An OWL-Based Specification of Database Management Systems 基于owl的数据库管理系统规范

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.021714

Sabin C. Buraga, D. Amariei, Octavian Dospinescu

引用次数: 5